NAMIC Wiki - User contributions [en]

2011 Winter Project Week:Atrial Fibrillation

2010-12-13T19:22:02Z

Behnood:

__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Georgia Tech: Behnood Gholami, Yi Gao, and Allen Tannenbaum

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as catheter ablation, specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

We are developing methods to segment the left atrial wall in delayed-enhanced MR imagery.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

We plan to finalize a fully-automatic segmentation approach to identify the blood pool in MRAs. The approach uses the robust statistics segmentation framework developed earlier at Georgia Tech.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==References==
* Y. Gao, B. Gholami, R. MacLeod, J. Blauer, W. M. Haddad, and A. R. Tannenbaum, "Segmentation of the Endocardial Wall of the Left Atrium using Localized Region-Based Active Contours and Statistical Shape Learning," Proc. SPIE Med. Imag., San Diego, CA, vol. 7623, 76234Z-1, 2010.

</div>

2011 Winter Project Week:Atrial Fibrillation

2010-12-13T19:21:03Z

Behnood:

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
</gallery>

==Instructions for Use of this Template==
#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
#Copy the entire text of this page into the page created above
#Link the created page into the list of projects for the project event
#Delete this section from the created page
#Send an email to tkapur at bwh.harvard.edu if you are stuck

==Key Investigators==
* Georgia Tech: Behnood Gholami, Yi Gao, and Allen Tannenbaum

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as catheter ablation, specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

We are developing methods to segment the left atrial wall in delayed-enhanced MR imagery.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

We plan to finalize a fully-automatic segmentation approach to identify the blood pool in MRAs. The approach uses the robust statistics segmentation framework developed earlier at Georgia Tech.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>

</div>
</div>

<div style="width: 97%; float: left;">

==References==
* Y. Gao, B. Gholami, R. MacLeod, J. Blauer, W. M. Haddad, and A. R. Tannenbaum, "Segmentation of the Endocardial Wall of the Left Atrium using Localized Region-Based Active Contours and Statistical Shape Learning," Proc. SPIE Med. Imag., San Diego, CA, vol. 7623, 76234Z-1, 2010.

</div>

2011 Winter Project Week:Atrial Fibrillation

2010-12-13T19:11:17Z

Behnood:

__NOTOC__
<gallery>
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
</gallery>

==Instructions for Use of this Template==
#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
#Copy the entire text of this page into the page created above
#Link the created page into the list of projects for the project event
#Delete this section from the created page
#Send an email to tkapur at bwh.harvard.edu if you are stuck

==Key Investigators==
* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
* Utah: Tom Fletcher, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>

Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.

Our plan for the project week is to first try out <bar>,...

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.

</div>
</div>

<div style="width: 97%; float: left;">

==Delivery Mechanism==

This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

#ITK Module
#Slicer Module
##Built-in
##Extension -- commandline
##Extension -- loadable
#Other (Please specify)

==References==
*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .

</div>

2011 Winter Project Week:Atrial Fibrillation

2010-12-13T19:11:08Z

Behnood: Created page with 'Test'

Test

2011 Winter Project Week

2010-12-13T19:10:11Z

Behnood: /* Segmentation */

Back to [[Project Events]], [[Events]]
Back to [[Project Events]], [[AHM_2011]], [[Events]]

__NOTOC__
[[image:PW-SLC2010.png|300px]]

== Dates.Venue.Registration ==

Please [[AHM_2011#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2011#Agenda|click here for the agenda for AHM 2011 and Project Week]].

==Background==

From January 10-14, 2011, the twelfth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

==Projects==

=== Segmentation ===
* Extension of ABC (Atlas-Based Classification) to detect pathology categories, with tests on TBI images (Bo Wang, Marcel Prastawa, Guido Gerig).
* [[2011_Winter_Project_Week:Atrial_Fibrillation|Segmentation of the left atrial wall for atrial fibrillation ablation therapy]] (Behnood Gholami, Yi Gao, and Allen Tannenbaum)
* The Vascular Modeling Toolkit in 3D Slicer (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper)
*TubeTK for vascular image segmentation and analysis (Stephen Aylward, Danielle Pace)
* A stenosis detector in Slicer4 using VMTK (Suares Tamekue, Daniel Haehn, Luca Antiga)
* [[2011_Winter_Project_Week:MeshCurvolver|Surface Region Segmentation for Surgical Planning and Mapping ]] (Peter Karasev, Karol Chudy, Allen Tannenbaum)
* Integration of SPECTRE into Slicer (Nicole Aucoin, Min Chen)

=== Registration ===
*[[2011_Winter_Project_Week:RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier,Ron Kikinis)
*[[2011_Winter_Project_Week:RegistrationAnisotropy|Voxel Anisotropy and Bias Field Effects on Slicer Image Registration]] (Dominik Meier, Andriy Fedorov) (tentative)
* Efficient co-registration of multiple MR modalities using the ABC (atlas-based classification) framework, joint visualization of multiple co-registered modalities (Bo Wang, Guido Gerig)
* DTI-MRI registration: Evaluation of large deformation diffeomorphic mapping (LDDMM) with other nonlinear registration schemes (Anuja Sharma, Guido Gerig)
* Registration of CT and MRI volumes for Adaptive Radiotherapy (Ivan Kolesov, Gregory Sharp, and Allen Tannenbaum)
* Atlas Registration in Slicer3 (Daniel Haehn, Dominik Meier, Kilian Pohl)
* Registration in the presence of anatomic variation (aka. Sliding organ registration) (Danielle Pace, Marc Niethammer, Petter Risholm, Tina Kapur, Sandy Wells, Stephen Aylward)
* [[2011_Winter_Project_Week:UncertaintyVisualization|Visualizing registration uncertainty in Slicer3]] (Petter Risholm, William Wells)
* [[2011_Winter_Project_Week:LandmarkRegularization|Landmark-based registration with analytic regularization]] (Nadya Shusharina, Gregory Sharp)

=== IGT ===
*Open IGT Link 2.0 (Junichi Tokuda, Nobuhiko Hata)
*Osteormark, navigation tool for Osteotomy (Laurent Chauvin, Nobuhiko Hata)
*[[2011_Winter_Project_Week:Intra-ProceduralProstateMotion|Detection and compensation for prostate motion during MR-guided prostate biopsy]] (A.Fedorov, Andras Lasso)
*Thin Client QT Interface for IGT (Nicholas Herlambang)
*Transform recorder and (surgical) procedure annotation module (Tamas Ungi, Junichi Tokuda)

=== Radiotherapy ===
* User controlled segmentation of head and neck structures for Adaptive Radiotherapy (Ivan Kolesov, Gregory Sharp, and Allen Tannenbaum)
* [[2011_Winter_Project_Week:DicomRtExport|DICOM-RT export]] (Greg Sharp, Steve Pieper)

=== Analysis ===
* [[2011_Winter_Project_Week:GAMBITCorticalThicknessAnalysis |GAMBIT - Cortical thickness analysis]] - Clement Vachet, Martin Styner
* [[2011_Winter_Project_Week:ParticleShapeAnalysis|Particle shape analysis incorporating surface normals ]] - Beatriz Paniagua, Martin Styner
* [[2011_Winter_Project_Week:NAMICShapeAnalysis |NAMIC shape analysis pipeline in Slicer 3]] - Lucile Bompard, Martin Styner, Chris Gloschat
* Particle Systems for Shape Analysis - Josh Cates, Manasi Datar, Ross Whitaker

=== Informatics ===
* [[2011_Winter_Project_Week:UIowaTHPDTIData|Share all UIowa Traveling Human Phantom DTI data with NAMIC]] - Mark Scully, Hans Johnson, Zack M.
* Ontology-augmented MRI brain atlas - Michael Halle, Jim Miller, Samira Farough
* Functional brain atlas (version 2) - Michael Halle, Jim Miller
* Annotation module in Slicer4: Display widget intersections (Daniel Haehn, Nicole Aucoin, Steve Pieper)

=== Diffusion ===
* [[2011_Winter_Project_Week:DicomToNrrdTestSuite |Test suite for DicomToNrrdConverter]] - Mark Scully, Zach Mullen, Xiaodong Tao, Hans Johnson
* [[2011_Winter_Project_Week:DicomToNrrdRefactoring |Requirements gathering for refactoring DicomToNrrdConverter]] - Mark Scully, Xiaodong Tao, Hans Johnson
* [[2011_Winter_Project_Week:DTIPrepDocumentation |Documentation and 1st Draft Tutorial for DTIPrep]] - Clement Vachet, Mark Scully, Hans Johnson
* Voxelwise fiber distribution from tractography - Yinpeng Li, Martin Styner
* Two-tensor full brain tractography pipeline - Lauren O'Donnell, Yogesh Rathi, C-F Westin
* Free-water elimination - Ofer Pasternak, Demian Wassermann, C-F Westin
* Finsler tractography in ITK - Antonio Tristan-Vega, C-F Westin
* Statistical analysis of Cingulum extracted using Volumetric framework - Gopal Veni, Ross Whitaker

=== NA-MIC Kit Internals ===

* Command line module logic redesign (passing paramenters, tie into workflows) - Jim, Steve
* 64bit Windows Builds - Dave P
* Dashboards: Factory machine, subprojects, and CDash@Home - Dave P, Zack M, Steve, and Stephen
* MIDAS for data hosting - Zach M and Hans
* vtkWidgets - JC and Will, Nicole Aucoin
* [[2011_Winter_Project_Week: Python and Slicer4]] Python and Slicer4: Workflows, Scripting, and Porting - JC, Jim, Steve, and Danielle
* Improve Performance of Slice Rendering in slicer3 and slicer4 (Steve, Will, Jc, Luca)

=== Execution Model ===
* Extract SlicerExecutionModel (SEM) into separate entity. SEM is the only component needed to build modules compatible with Slicer3D, so it should be easy incorporate into external applications without all of Slicer3D. Jim, Hans
* [[2011_Winter_Project_Week:ExtendSEMXml|Extend SEM xml]] to include sections for explicit grant acknowledgements, pointers to documentation, and pointers to examples. - Hans, Andriy
* [[2011_Winter_Project_Week:SEMXMLSchema|Create a formal schema for the SEM xml so that eternal tools (i.e. nipype) can validate the xml.]] - Hans Johnson, Jim Miller, Tim Olsen
* [[2011_Winter_Project_Week:XMLToMediaWiki|Improve documentation extractor script that converts XML to MediaWiki format so that it can directly push this information into the Slicer3D MediaWiki.]] - (Wiki Systems Admin), Hans Johnson
* [[2011_Winter_Project_Week:ExternalToolsMergingStrategies | Improve merging strategies between software that is part of externals tools and part of Slicer.]] - Mark Scully, Hans Johnson

=== Workflows and Integration ===
* Workflows and Service Oriented Architecture Solutions for Slicer3 Modules. - Alexander Zaitsev, Wendy Plesniak, Charles Guttmann, Ron Kikinis

=== Preparation ===

#Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
#Starting Thursday, October 28th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 28: Engineering Infrastructure Projects
#*November 4: Engineering Infrastructure Projects
#*November 11: DPB Projects: Iowa, Outcomes from Alg Core Retreat
#*November 18: DPB Projects: MGH
#*November 25: DBP Projects, Funded External Collaborations
#*December 2: Funded External Collaborations
#*December 9: Other External Collaborations
#*December 16:Finalize Engineering Projects
#*January 6: Loose Ends
#By December 16, 2010: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
#By December 16, 2010: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
##Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
##Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
##Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
#Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

2011 Winter Project Week

2010-11-17T20:09:54Z

Behnood: /* Segmentation */

Projects:PainAssessment

2010-11-12T18:11:56Z

Behnood: /* Publications */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Agitation and Pain Assessment Using Digital Imaging =

= Description =

Pain assessment in patients who are unable to verbally
communicate with the medical staff is a challenging problem
in patient critical care. This problem is most prominently
encountered in sedated patients in the intensive care unit
(ICU) recovering from trauma and major surgery, as well
as infant patients and patients with brain injuries.
Current practice in the ICU requires the nursing staff in
assessing the pain and agitation experienced by the patient,
and taking appropriate action to ameliorate the patient’s
anxiety and discomfort.

The fundamental limitations in sedation and pain assessment
in the ICU stem from subjective assessment criteria,
rather than quantifiable, measurable data for ICU sedation.
This often results in poor quality and inconsistent treatment
of patient agitation from nurse to nurse. Recent advances
in computer vision techniques can assist the medical staff
in assessing sedation and pain by constantly monitoring the
patient and providing the clinician with quantifiable data for
ICU sedation. An automatic pain assessment system can be
used within a decision support framework which can also
provide automated sedation and analgesia in the ICU.
In order to achieve closed-loop sedation control in the ICU,
a quantifiable feedback signal is required that reflects some
measure of the patient’s agitation. A non-subjective agitation
assessment algorithm can be a key component in developing
closed-loop sedation control algorithms for ICU sedation.

Individuals in pain manifest their condition through "pain
behavior", which includes facial expressions. Clinicians regard the patient’s facial expression as a valid indicator for
pain and pain intensity. Hence, correct interpretation of
the facial expressions of the patient and its correlation with
pain is a fundamental step in designing an automated pain
assessment system. Of course, other pain behaviors including
head movement and the movement of other body parts, along
with physiological indicators of pain, such as heart rate,
blood pressure, and respiratory rate responses should also
be included in such a system.

Computer vision techniques can be used to quantify agitation
in sedated ICU patients. In particular, such techniques
can be used to develop objective agitation measurements
from patient motion. In the case of paraplegic patients, whole
body movement is not available, and hence, monitoring the
whole body motion is not a viable solution. In this case,
measuring head motion and facial grimacing for quantifying
patient agitation in critical care can be a useful alternative.

== Pain Recognition using Sparse Kernel Machines ==

Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were
used to identify the facial expressions corresponding to pain.
A total of 21 subjects from the infant COPE database were
selected such that for each subject at least one photograph
corresponded to pain and one to non-pain. The total number
of photographs available for each subject ranged between 5
to 12, with a total of 181 photographs considered. We applied
the leave-one-out method for validation.

The classification accuracy for the SVM algorithm
with a linear kernel was 90%. Applying the RVM algorithm
with a linear kernel to the same data set resulted in an
almost identical classification accuracy, namely, 91%.

== Pain Intensity Assessment ==

In addition to classification, the RVM algorithm provides
the posterior probability of the membership of a test image to
a class. As discussed earlier, using a Bayesian interpretation
of probability, the probability of an event can be interpreted
as the degree of the uncertainty associated with such an
event. This uncertainty can be used to estimate pain intensity.

In particular, if a classifier is trained with a series of facial
images corresponding to pain and non-pain, then there is
some uncertainty for associating the facial image of a person
experiencing moderate pain to the pain class. The efficacy
of such an interpretation of the posterior probability was
validated by comparing the algorithm’s pain assessment
with that assessed by several experts (intensivists) and nonexperts.

In order to compare the pain intensity assessment given by
the RVM algorithm with human assessment, we compared
the subjective measurement of the pain intensity assessed
by expert and non-expert examiners with the uncertainty in
the pain class membership (posterior probability) given by
the RVM algorithm. We chose 5 random infants from the
COPE database, and for each subject two photographs of
the face corresponding to the non-pain and pain conditions
were selected. In the selection process, photographs were
selected where the infant’s facial expression truly reflected
the pain condition—calm for non-pain and distressed for
pain—and a score of 0 and 100, respectively, was assigned
to these photographs to give the human examiner a fair prior
knowledge for the assessment of the pain intensity.

Ten data examiners were asked to provide a score ranging
from 0 to 100 for each new photograph of the same subject,
using a multiple of 10 for the scores. Five examiners with no
medical expertise and five examiners with medical expertise
were selected for this assessment. The medical experts were
members of the clinical staff at the intensive care unit of
the Northeast Georgia Medical Center, Gainesville, GA,
consisting of one medical doctor, one nurse practitioner, and
three nurses. They were asked to assess the pain for a series
of random photographs of the same subject, with the criterion
that a score above 50 corresponds to pain, and with the higher
score corresponding to a higher pain intensity. Analogously,
a score below 50 corresponds to non-pain, with the higher
score corresponding to a higher level of discomfort. The
posterior probability given by the RVM algorithm with a
linear kernel for each corresponding photograph was rounded
off to the nearest multiple of 10.

[[Image:Pain1.JPG|500px]]
[[Image:pain2.JPG|500px]]

= Publications =

B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466.

B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

= Key Investigators =

Georgia Tech: Behnood Gholami, Wassim M. Haddad, and Allen Tannenbaum

Algorithm:GATech

2010-11-12T18:11:26Z

Behnood: /* Agitation and Pain Assessment Using Digital Imaging */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.
|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Left Atrial Wall for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., vol. 57, pp. 1457-1466.

B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening (inactive)]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-11-12T18:10:56Z

Behnood: /* Agitation and Pain Assessment Using Digital Imaging */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.
|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Left Atrial Wall for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.

B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening (inactive)]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:PainAssessment

2010-11-12T18:10:02Z

Behnood: /* Publications */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Agitation and Pain Assessment Using Digital Imaging =

= Description =

Pain assessment in patients who are unable to verbally
communicate with the medical staff is a challenging problem
in patient critical care. This problem is most prominently
encountered in sedated patients in the intensive care unit
(ICU) recovering from trauma and major surgery, as well
as infant patients and patients with brain injuries.
Current practice in the ICU requires the nursing staff in
assessing the pain and agitation experienced by the patient,
and taking appropriate action to ameliorate the patient’s
anxiety and discomfort.

The fundamental limitations in sedation and pain assessment
in the ICU stem from subjective assessment criteria,
rather than quantifiable, measurable data for ICU sedation.
This often results in poor quality and inconsistent treatment
of patient agitation from nurse to nurse. Recent advances
in computer vision techniques can assist the medical staff
in assessing sedation and pain by constantly monitoring the
patient and providing the clinician with quantifiable data for
ICU sedation. An automatic pain assessment system can be
used within a decision support framework which can also
provide automated sedation and analgesia in the ICU.
In order to achieve closed-loop sedation control in the ICU,
a quantifiable feedback signal is required that reflects some
measure of the patient’s agitation. A non-subjective agitation
assessment algorithm can be a key component in developing
closed-loop sedation control algorithms for ICU sedation.

Individuals in pain manifest their condition through "pain
behavior", which includes facial expressions. Clinicians regard the patient’s facial expression as a valid indicator for
pain and pain intensity. Hence, correct interpretation of
the facial expressions of the patient and its correlation with
pain is a fundamental step in designing an automated pain
assessment system. Of course, other pain behaviors including
head movement and the movement of other body parts, along
with physiological indicators of pain, such as heart rate,
blood pressure, and respiratory rate responses should also
be included in such a system.

Computer vision techniques can be used to quantify agitation
in sedated ICU patients. In particular, such techniques
can be used to develop objective agitation measurements
from patient motion. In the case of paraplegic patients, whole
body movement is not available, and hence, monitoring the
whole body motion is not a viable solution. In this case,
measuring head motion and facial grimacing for quantifying
patient agitation in critical care can be a useful alternative.

== Pain Recognition using Sparse Kernel Machines ==

Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were
used to identify the facial expressions corresponding to pain.
A total of 21 subjects from the infant COPE database were
selected such that for each subject at least one photograph
corresponded to pain and one to non-pain. The total number
of photographs available for each subject ranged between 5
to 12, with a total of 181 photographs considered. We applied
the leave-one-out method for validation.

The classification accuracy for the SVM algorithm
with a linear kernel was 90%. Applying the RVM algorithm
with a linear kernel to the same data set resulted in an
almost identical classification accuracy, namely, 91%.

== Pain Intensity Assessment ==

In addition to classification, the RVM algorithm provides
the posterior probability of the membership of a test image to
a class. As discussed earlier, using a Bayesian interpretation
of probability, the probability of an event can be interpreted
as the degree of the uncertainty associated with such an
event. This uncertainty can be used to estimate pain intensity.

In particular, if a classifier is trained with a series of facial
images corresponding to pain and non-pain, then there is
some uncertainty for associating the facial image of a person
experiencing moderate pain to the pain class. The efficacy
of such an interpretation of the posterior probability was
validated by comparing the algorithm’s pain assessment
with that assessed by several experts (intensivists) and nonexperts.

In order to compare the pain intensity assessment given by
the RVM algorithm with human assessment, we compared
the subjective measurement of the pain intensity assessed
by expert and non-expert examiners with the uncertainty in
the pain class membership (posterior probability) given by
the RVM algorithm. We chose 5 random infants from the
COPE database, and for each subject two photographs of
the face corresponding to the non-pain and pain conditions
were selected. In the selection process, photographs were
selected where the infant’s facial expression truly reflected
the pain condition—calm for non-pain and distressed for
pain—and a score of 0 and 100, respectively, was assigned
to these photographs to give the human examiner a fair prior
knowledge for the assessment of the pain intensity.

Ten data examiners were asked to provide a score ranging
from 0 to 100 for each new photograph of the same subject,
using a multiple of 10 for the scores. Five examiners with no
medical expertise and five examiners with medical expertise
were selected for this assessment. The medical experts were
members of the clinical staff at the intensive care unit of
the Northeast Georgia Medical Center, Gainesville, GA,
consisting of one medical doctor, one nurse practitioner, and
three nurses. They were asked to assess the pain for a series
of random photographs of the same subject, with the criterion
that a score above 50 corresponds to pain, and with the higher
score corresponding to a higher pain intensity. Analogously,
a score below 50 corresponds to non-pain, with the higher
score corresponding to a higher level of discomfort. The
posterior probability given by the RVM algorithm with a
linear kernel for each corresponding photograph was rounded
off to the nearest multiple of 10.

[[Image:Pain1.JPG|500px]]
[[Image:pain2.JPG|500px]]

= Publications =

B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.

B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

= Key Investigators =

Georgia Tech: Behnood Gholami, Wassim M. Haddad, and Allen Tannenbaum

Projects:PainAssessment

2010-11-12T18:09:49Z

Behnood: /* Publications */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
= Agitation and Pain Assessment Using Digital Imaging =

= Description =

Pain assessment in patients who are unable to verbally
communicate with the medical staff is a challenging problem
in patient critical care. This problem is most prominently
encountered in sedated patients in the intensive care unit
(ICU) recovering from trauma and major surgery, as well
as infant patients and patients with brain injuries.
Current practice in the ICU requires the nursing staff in
assessing the pain and agitation experienced by the patient,
and taking appropriate action to ameliorate the patient’s
anxiety and discomfort.

The fundamental limitations in sedation and pain assessment
in the ICU stem from subjective assessment criteria,
rather than quantifiable, measurable data for ICU sedation.
This often results in poor quality and inconsistent treatment
of patient agitation from nurse to nurse. Recent advances
in computer vision techniques can assist the medical staff
in assessing sedation and pain by constantly monitoring the
patient and providing the clinician with quantifiable data for
ICU sedation. An automatic pain assessment system can be
used within a decision support framework which can also
provide automated sedation and analgesia in the ICU.
In order to achieve closed-loop sedation control in the ICU,
a quantifiable feedback signal is required that reflects some
measure of the patient’s agitation. A non-subjective agitation
assessment algorithm can be a key component in developing
closed-loop sedation control algorithms for ICU sedation.

Individuals in pain manifest their condition through "pain
behavior", which includes facial expressions. Clinicians regard the patient’s facial expression as a valid indicator for
pain and pain intensity. Hence, correct interpretation of
the facial expressions of the patient and its correlation with
pain is a fundamental step in designing an automated pain
assessment system. Of course, other pain behaviors including
head movement and the movement of other body parts, along
with physiological indicators of pain, such as heart rate,
blood pressure, and respiratory rate responses should also
be included in such a system.

Computer vision techniques can be used to quantify agitation
in sedated ICU patients. In particular, such techniques
can be used to develop objective agitation measurements
from patient motion. In the case of paraplegic patients, whole
body movement is not available, and hence, monitoring the
whole body motion is not a viable solution. In this case,
measuring head motion and facial grimacing for quantifying
patient agitation in critical care can be a useful alternative.

== Pain Recognition using Sparse Kernel Machines ==

Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were
used to identify the facial expressions corresponding to pain.
A total of 21 subjects from the infant COPE database were
selected such that for each subject at least one photograph
corresponded to pain and one to non-pain. The total number
of photographs available for each subject ranged between 5
to 12, with a total of 181 photographs considered. We applied
the leave-one-out method for validation.

The classification accuracy for the SVM algorithm
with a linear kernel was 90%. Applying the RVM algorithm
with a linear kernel to the same data set resulted in an
almost identical classification accuracy, namely, 91%.

== Pain Intensity Assessment ==

In addition to classification, the RVM algorithm provides
the posterior probability of the membership of a test image to
a class. As discussed earlier, using a Bayesian interpretation
of probability, the probability of an event can be interpreted
as the degree of the uncertainty associated with such an
event. This uncertainty can be used to estimate pain intensity.

In particular, if a classifier is trained with a series of facial
images corresponding to pain and non-pain, then there is
some uncertainty for associating the facial image of a person
experiencing moderate pain to the pain class. The efficacy
of such an interpretation of the posterior probability was
validated by comparing the algorithm’s pain assessment
with that assessed by several experts (intensivists) and nonexperts.

In order to compare the pain intensity assessment given by
the RVM algorithm with human assessment, we compared
the subjective measurement of the pain intensity assessed
by expert and non-expert examiners with the uncertainty in
the pain class membership (posterior probability) given by
the RVM algorithm. We chose 5 random infants from the
COPE database, and for each subject two photographs of
the face corresponding to the non-pain and pain conditions
were selected. In the selection process, photographs were
selected where the infant’s facial expression truly reflected
the pain condition—calm for non-pain and distressed for
pain—and a score of 0 and 100, respectively, was assigned
to these photographs to give the human examiner a fair prior
knowledge for the assessment of the pain intensity.

Ten data examiners were asked to provide a score ranging
from 0 to 100 for each new photograph of the same subject,
using a multiple of 10 for the scores. Five examiners with no
medical expertise and five examiners with medical expertise
were selected for this assessment. The medical experts were
members of the clinical staff at the intensive care unit of
the Northeast Georgia Medical Center, Gainesville, GA,
consisting of one medical doctor, one nurse practitioner, and
three nurses. They were asked to assess the pain for a series
of random photographs of the same subject, with the criterion
that a score above 50 corresponds to pain, and with the higher
score corresponding to a higher pain intensity. Analogously,
a score below 50 corresponds to non-pain, with the higher
score corresponding to a higher level of discomfort. The
posterior probability given by the RVM algorithm with a
linear kernel for each corresponding photograph was rounded
off to the nearest multiple of 10.

[[Image:Pain1.JPG|500px]]
[[Image:pain2.JPG|500px]]

= Publications =

'' In Press''

B. Gholami, W. M. Haddad, and A. Tannenbaum, “Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging,” IEEE Trans. Biomed. Eng., Vol. 57, 1457-1466.

B. Gholami, W. M. Haddad, and A. R. Tannenbaum, “Agitation and Pain Assessment Using Digital Imaging,” Proc. IEEE Eng. Med. Biolog. Conf., Minneapolis, MN, pp. 2176-2179, 2009 (Awarded National Institute of Biomedical Imaging and Bioengineering/National Institute of Health Student Travel Fellowship).

= Key Investigators =

Georgia Tech: Behnood Gholami, Wassim M. Haddad, and Allen Tannenbaum

Algorithm:GATech

2010-11-12T18:08:53Z

Behnood: /* Agitation and Pain Assessment Using Digital Imaging */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.
|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Left Atrial Wall for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', vol. 57, 1457-1466..

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening (inactive)]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-11-12T18:06:29Z

Behnood: /* Segmentation of the Endocardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.
|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Left Atrial Wall for Atrial Fibrillation Ablation Therapy]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction using Tubular Surface Segmentation. September 2009. Proceedings of the Workshop on Cardiac Interventional Imaging and Biophysical Modelling (CI2BM'09), Int Conf Med Image Comput Comput Assist Interv. 2009.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki, D. Terry and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010.

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of neural fiber bundles towards schizophrenia detection and characterization, using the Tubular Surface model. Neuroimage (in preparation)

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening (inactive)]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:SegmentationEndocardialWall

2010-11-12T17:54:03Z

Behnood: /* Future Work */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

== Future Work ==

The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately-after-ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation of the left atrial wall, and the blood pool MRA as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involves recurrence of the atrial fibrillation (yes/no), the time of recurrence after the procedure (x number of months), and any other relevant clinical data which its prediction could assist the physician before the ablation procedure (e.g., the severity of recurrence, etc.). The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.

The Georgia Tech team proposed using machine learning techniques to predict the success of the ablation procedure and time of recurrence (among other information of interest for the physician) based on enhancements in the left atrial wall in pre-ablation, and immediately-after-procedure DE-MRI's. The feasibility of using pre-ablation DE-MRI for procedure success in already published by the Utah team.

= Publications =

* Gao Y., Gholami B., MacLeod R.S., Blauer J., Haddad W.M., Tannenbaum A. [http://www.na-mic.org/publications/item/view/1844 Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning.] Proceedings of SPIE Medical Imaging 2010.

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum

University of Utah: Rob MacLeod, Josh Blauer, and Josh Cates

Projects:SegmentationEndocardialWall

2010-11-12T17:53:24Z

Behnood: /* Future Work */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

== Future Work ==

The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately-after-ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation of the left atrial wall, and the blood pool MRA as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involves recurrence of the atrial fibrillation (yes/no), the time of recurrence after the procedure (x number of months), and any other relevant clinical data which its prediction could assist the physician before the ablation procedure (e.g., the severity of recurrence, etc.). The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.

The Georgia Tech team proposed using machine learning techniques to predict the success of the ablation procedure and time of recurrence (among other information of interest for the physician) based on enhancements in the left atrial wall in pre-ablation, and immediately-after-procedure DE-MRI's. The feasibility of using pre-ablation MRI for procedure success in already published by the Utah team.

= Publications =

* Gao Y., Gholami B., MacLeod R.S., Blauer J., Haddad W.M., Tannenbaum A. [http://www.na-mic.org/publications/item/view/1844 Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning.] Proceedings of SPIE Medical Imaging 2010.

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum

University of Utah: Rob MacLeod, Josh Blauer, and Josh Cates

Projects:SegmentationEndocardialWall

2010-11-12T17:47:05Z

Behnood:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

== Future Work ==

The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately after ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation of the left atrial wall as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involve recurrence of the atrial fibrillation, the time of recurrence after the procedure, and any other relevant clinical data which its prediction could assist the physician before the ablation procedure. The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.

The Georgia Tech team proposed using ideas in machine learning to predict the success of the ablation procedure (among other information of interest for the physician) based on pre-ablation MRI. The Utah team has already published results indicating the significance of pre-ablation DE-MRI in the outcome of the ablation therapy.

= Publications =

* Gao Y., Gholami B., MacLeod R.S., Blauer J., Haddad W.M., Tannenbaum A. [http://www.na-mic.org/publications/item/view/1844 Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning.] Proceedings of SPIE Medical Imaging 2010.

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum

University of Utah: Rob MacLeod, Josh Blauer, and Josh Cates

Projects:SegmentationEndocardialWall

2010-11-12T17:44:56Z

Behnood:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

== Future Work ==

The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately after ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involve recurrence of the atrial fibrillation, the time of recurrence after the procedure, and any other relevant clinical data which its prediction could assist the physician before the ablation procedure. The Utah team agreed to provide the data in a week. The two teams agreed to exchange their latest works/publications regarding the atrial fibrillation project.

The Georgia Tech team proposed using ideas in machine learning to predict the success of the ablation procedure (among other information of interest for the physician) based on pre-ablation MRI. The Utah team has already published results indicating the significance of pre-ablation DE-MRI in the outcome of the ablation therapy.

= Publications =

* Gao Y., Gholami B., MacLeod R.S., Blauer J., Haddad W.M., Tannenbaum A. [http://www.na-mic.org/publications/item/view/1844 Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning.] Proceedings of SPIE Medical Imaging 2010.

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum

University of Utah: Rob MacLeod, Josh Blauer, and Josh Cates

Projects:SegmentationEndocardialWall

2010-11-12T17:40:48Z

Behnood:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

== Future Work ==

The two teams from Georgia Tech and University of Utah held a teleconference on November 10, 2010. The Georgia Tech team requested 3-month pre-ablation, immediately after ablation, and 3-month post-ablation DE-MRI, their corresponding hand segmentation as well as the outcome of the ablation procedure for the DE-MRI's provided. The specific information of interest with regards to the procedure outcome involve recurrence of the atrial fibrillation, the time of recurrence after the procedure, and any other relevant clinical data which its prediction could assist the physician before the ablation procedure. The Georgia Tech team proposed using

= Publications =

* Gao Y., Gholami B., MacLeod R.S., Blauer J., Haddad W.M., Tannenbaum A. [http://www.na-mic.org/publications/item/view/1844 Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning.] Proceedings of SPIE Medical Imaging 2010.

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum

University of Utah: Rob MacLeod, and Josh Blauer

Projects:SegmentationEpicardialWall

2010-02-09T23:31:13Z

Behnood: Blanked the page

Algorithm:GATech

2010-02-08T18:17:28Z

Behnood: /* Georgia Tech Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.
|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:16:47Z

Behnood: /* Segmentation of the Endocardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this research we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:SegmentationEndocardialWall

2010-02-08T18:16:28Z

Behnood: /* Description */

Algorithm:GATech

2010-02-08T18:15:51Z

Behnood: /* Segmentation of the Endocardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:14:55Z

Behnood: /* Georgia Tech Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:14:40Z

Behnood: /* Georgia Tech Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:3D_Segmentation_LA.png.JPG|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:14:08Z

Behnood: Undo revision 48460 by Behnood (Talk)

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:11:03Z

Behnood: /* Segmentation of the Endocardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging 2010.

|-

| | [[Image:3D_Segmentation_LA.png|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:SegmentationEndocardialWall

2010-02-08T18:10:43Z

Behnood: Created page with 'Back to Georgia Tech Algorithms __NOTOC__ <gallery> Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall Image:2d_axial_LA.png | 2D…'

Algorithm:GATech

2010-02-08T18:10:32Z

Behnood: /* Segmentation of the Endocardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEndocardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Projects:SegmentationEpicardialWall

2010-02-08T18:09:29Z

Behnood: /* Key Investigators */

Projects:SegmentationEpicardialWall

2010-02-08T18:09:21Z

Behnood: /* Key Investigators */

Projects:SegmentationEpicardialWall

2010-02-08T18:09:09Z

Behnood: /* Publications */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Wassim M. Haddad, and Allen Tannenbaum, Georgia Tech
* Rob MacLeod, and Josh Blauer, University of Utah

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

= Publications =

Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging, San Diego, CA, 2010.

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum
University of Utah: Rob MacLeod, and Josh Blauer

Projects:SegmentationEpicardialWall

2010-02-08T18:07:46Z

Behnood: /* Our Approach */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Wassim M. Haddad, and Allen Tannenbaum, Georgia Tech
* Rob MacLeod, and Josh Blauer, University of Utah

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

A powerful approach in medical image segmentation is active contour modeling wherein the boundaries of an object of interest are captured by minimizing an energy functional. The segmentation of the endocardial wall of the left atrium in delayed-enhancement magnetic resonance images (DE-MRI) using active contours is a challenging problem mainly due to the absence of clear boundaries. This usually leads either to contour ''leaks'', where the contour expands beyond the desired boundary, or partial segmentation, where the contour only captures the desired area partially. A shape-based segmentation approach can overcome this problem by using prior shape knowledge in the segmentation process. In this research, we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.

= Publications =

''In Press''

Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum
University of Utah: Rob MacLeod, and Josh Blauer

Projects:SegmentationEpicardialWall

2010-02-08T18:06:53Z

Behnood: /* Description */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Wassim M. Haddad, and Allen Tannenbaum, Georgia Tech
* Rob MacLeod, and Josh Blauer, University of Utah

= Description =

Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs are used to manage this condition, but suffer from low success rates and involve major side effects. In an alternative treatment, known as ''catheter ablation'', specific parts of the left atrium are targeted for radio frequency ablation using an intracardiac catheter. Application of radio frequency energy to the cardiac tissue causes thermal injury (lesions), which in turn results into scar tissue. Successful ablation can eliminate, or isolate, the problematic sources of electrical activity and effectively cure atrial fibrillation.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. MRI can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we propose a shape-based image segmentation framework to segment the endocardial wall of the left atrium.

== Our Approach ==

Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.

= Publications =

''In Press''

Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum
University of Utah: Rob MacLeod, and Josh Blauer

Projects:SegmentationEpicardialWall

2010-02-08T18:05:14Z

Behnood: /* Key Investigators */

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Wassim M. Haddad, and Allen Tannenbaum, Georgia Tech
* Rob MacLeod, and Josh Blauer, University of Utah

= Description =

Atrial fibrillation (AF), which is caused by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs can be used to control this condition but it involves side effects and only 40-60% of the AF population is maintained in regular rhythm one year after such treatment. An alternative treatment is catheter ablation in which the sources of electrical dyssynchrony are suppressed by ablating specific tissues using a special catheter. Ablation is performed by exposing the tissue to a high temperature, which results in lesion (scar) formation. This procedure, if successful, provides a final cure to AF.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.

== Our Approach ==

Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.

= Publications =

''In Press''

Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum
University of Utah: Rob MacLeod, and Josh Blauer

Projects:SegmentationEpicardialWall

2010-02-08T18:04:51Z

Behnood:

Back to [[Algorithm:GATech|Georgia Tech Algorithms]]
__NOTOC__
<gallery>
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Allen Tannenbaum, Georgia Tech
* Rob MacLeod, Josh Blauer, University of Utah

= Description =

Atrial fibrillation (AF), which is caused by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs can be used to control this condition but it involves side effects and only 40-60% of the AF population is maintained in regular rhythm one year after such treatment. An alternative treatment is catheter ablation in which the sources of electrical dyssynchrony are suppressed by ablating specific tissues using a special catheter. Ablation is performed by exposing the tissue to a high temperature, which results in lesion (scar) formation. This procedure, if successful, provides a final cure to AF.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.

== Our Approach ==

Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.

= Publications =

''In Press''

Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

= Key Investigators =

Georgia Tech: Behnood Gholami, Yi Gao, Wassim Haddad, and Allen Tannenbaum
University of Utah: Rob MacLeod, and Josh Blauer

Algorithm:GATech

2010-02-08T18:02:19Z

Behnood: /* Segmentation of the Epicardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEpicardialWall|Segmentation of the Endocardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Endocardial Wall of the Left Atrium using Local Region-Based Active Contours and Statistical Shape Learning, SPIE Medical Imaging 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:01:15Z

Behnood: /* Segmentation of the Epicardial Wall of the Left Atrium */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEpicardialWall|Segmentation of the Epicardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using
Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:00:55Z

Behnood: /* Agitation and Pain Assessment Using Digital Imaging */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (accepted for publication).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEpicardialWall|Segmentation of the Epicardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using
Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T18:00:26Z

Behnood: /* Georgia Tech Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (in submission).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEpicardialWall|Segmentation of the Epicardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using
Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

Algorithm:GATech

2010-02-08T17:59:38Z

Behnood: /* Georgia Tech Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Georgia Tech Algorithms (PI: Allen Tannenbaum) =

At Georgia Tech, we are broadly interested in a range of mathematical image analysis algorithms for segmentation, registration, diffusion-weighted MRI analysis, and statistical analysis. For many applications, we cast the problem in an energy minimization framework wherein we define a partial differential equation whose numeric solution corresponds to the desired algorithmic outcome. The following are many examples of PDE techniques applied to medical image analysis.

= Georgia Tech Projects =

|-

| | [[Image:Pain1.JPG|200px]]
| |

== [[Projects:PainAssessment|Agitation and Pain Assessment Using Digital Imaging]] ==

Pain assessment in patients who are unable to verbally
communicate with medical staff is a challenging problem
in patient critical care. The fundamental limitations in sedation
and pain assessment in the intensive care unit (ICU) stem
from subjective assessment criteria, rather than quantifiable,
measurable data for ICU sedation and analgesia. This often
results in poor quality and inconsistent treatment of patient
agitation and pain from nurse to nurse. Recent advancements in
pattern recognition techniques using a relevance vector machine
algorithm can assist medical staff in assessing sedation and pain
by constantly monitoring the patient and providing the clinician
with quantifiable data for ICU sedation. In this paper, we show
that the pain intensity assessment given by a computer classifier
has a strong correlation with the pain intensity assessed by
expert and non-expert human examiners.[[Projects:PainAssessment|More...]]

'''New: ''' B. Gholami, W. M. Haddad, and A. R. Tannenbaum, Agitation and Pain Assessment Using Digital Imaging, ''IEEE Tran. Biomed. Eng.'', (in submission).

|-

| | [[Image:afib.JPG|200px]]
| |

== [[Projects:SegmentationEpicardialWall|Segmentation of the Epicardial Wall of the Left Atrium]] ==

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation.
Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.[[Projects:SegmentationEpicardialWall|More...]]

'''New: ''' Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using
Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010 (in submission).

{| cellpadding="10" style="text-align:left;"

| style="width:15%" | [[Image:GT-SPD-img1.png|200px|]]
| style="width:85%" |

== [[Projects:SoftPlaqueDetection|Soft Plaque Detection in CTA Imagery]] ==

The ability to detect and measure non-calciﬁed plaques (also known as soft plaques) may improve physicians’ ability to predict cardiac events. This work automatically detects soft plaques in CTA imagery using active contours driven by spatially localized probabilistic models. Plaques are identified by simultaneously segmenting the vessel from the inside-out and the outside-in using carefully chosen localized energies [[Projects:SoftPlaqueDetection|More...]]

'''New: ''' Soft Plaque Detection and Automatic Vessel Segmentation. PMMIA Workshop in MICCAI, Sep. 2009.

|-

| | [[Image:GT-DWI-Reorientation-1.jpg|200px]]
| |

== [[Projects:DWIReorientation|Re-Orientation Approach for Segmentation of DW-MRI]] ==

This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. [[Projects:DWIReorientation|More...]]

'''New: ''' Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation. Neuroimage, Mar 2009.

|-

| | [[Image:GTTubSurfaceSeg-Img1.png|200px]]
| |

== [[Projects:TubularSurfaceSegmentation|Tubular Surface Segmentation Framework]] ==

We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. [[Projects:TubularSurfaceSegmentation|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Tubular Surface Evolution for Segmentation of the Cingulum Bundle From DW-MRI. September 2008. Proceedings of the Second Workshop on Mathematical Foundations of Computational Anatomy (MFCA'08), Int Conf Med Image Comput Comput Assist Interv. 2008.

'''New: ''' V. Mohan, G. Sundaramoorthi and A. Tannenbaum. Tubular Surface Segmentation for Extracting Anatomical Structures from Medical Imagery (in submission). IEEE Transactions on Medical Imaging.

|-

| | [[Image:GT-PopStudyVis OnCBs Case19-View2.jpg|200px]]
| |

== [[Projects:TubularSurfaceSegmentationPopStudy|Group Study on DW-MRI using the Tubular Surface Model]] ==

We have proposed a new framework for performing group studies on DW-MRI data sets using the Tubular Surface Model of Mohan et al. We successfully apply this framework to discriminating schizophrenic cases from normal controls, as well as towards visualizing the regions of the Cingulum Bundle that are affected by Schizophrenia. [[Projects:TubularSurfaceSegmentationPopStudy|More...]]

'''New: ''' V. Mohan, G. Sundaramoorthi, M. Kubicki and A. Tannenbaum. Population Analysis of the Cingulum Bundle using the Tubular Surface Model for Schizophrenia Detection. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:RadOnc HN seg.png|200px]]
| |

== [[Projects:MGH-HeadAndNeck-RT|Adaptive Radiotherapy for head, neck and thorax]] ==

We proposed an algorithm to include prior knowledge in previously segmented anatomical structures to help in the segmentation of the next structure. This will add enough prior information to allow the Graph Cuts algorithm to segment structures with fuzzy boundaries. [[Projects:MGH-HeadAndNeck-RT|More...]]

'''New: ''' I. Kolesov, V. Mohan, G. Sharp and A. Tannenbaum. Graph Cut Segmentation Based on Local Statistics Using Anatomical Landmarks. SPIE Medical Imaging 2010 (in submission).

|-

| | [[Image:Circle seg.PNG|200px|]]
| |

== [[Projects:KPCASegmentation|Kernel PCA for Segmentation]] ==

Segmentation performances using active contours can be drastically improved if the possible shapes of the object of interest are learnt. The goal of this work is to use Kernel PCA to learn shape priors. Kernel PCA allows for learning non linear dependencies in data sets, leading to more robust shape priors. [[Projects:KPCASegmentation|More...]]

'''New: ''' S. Dambreville, Y. Rathi, and A. Tannenbaum. A Framework for Image Segmentation using Image Shape Models and Kernel PCA Shape Priors. IEEE Trans Pattern Anal Mach Intell. 2008 Aug;30(8):1385-99

|-
| | [[Image:ZoomedResultWithModel.png|200px]]
| |

== [[Projects:GeodesicTractographySegmentation|Geodesic Tractography Segmentation]] ==

In this work, we provide an energy minimization framework which allows one to find fiber tracts and volumetric fiber bundles in brain diffusion-weighted MRI (DW-MRI). [[Projects:GeodesicTractographySegmentation|More...]]

'''New: ''' J. Melonakos, E. Pichon, S. Angenet, and A. Tannenbaum. Finsler Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, March 2008, Vol 30, Num 3.

|-

| | [[Image:P1_small.png|200px|]]
| |

== [[Projects:LabelSpace|Label Space: A Coupled Multi-Shape Representation]] ==

Many techniques for multi-shape representation may often develop inaccuracies stemming from either approximations or inherent variation. Label space is an implicit representation that offers unbiased algebraic manipulation and natural expression of label uncertainty. We demonstrate smoothing and registration on multi-label brain MRI. [[Projects:LabelSpace|More...]]

'''New: ''' J. Malcolm, Y. Rathi, A. Tannenbaum. "Label Space: A Multi-Object Shape Representation." In Combinatorial Image Analysis, 2008.

|-

| | [[Image:BasePair3DModel.JPG|200px|]]
| |

== [[Projects:NonParametricClustering|Non Parametric Clustering for Biomolecular Structural Analysis]] ==

High accuracy imaging and image processing techniques allow for collecting structural information of biomolecules with atomistic accuracy. Direct interpretation of the dynamics and the functionality of these structures with physical models, is yet to be developed. Clustering of molecular conformations into classes seems to be the first stage in recovering the formation and the functionality of these molecules. [[Projects:NonParametricClustering|More...]]

'''New: ''' E. Hershkovits, A. Tannenbaum, and R. Tannenbaum. Adsorption of Block Copolymers from Selective Solvents on Curved Surfaces. Macromolecules. 2008.

|-

| | [[Image:TruckInitialization.png|200px|]]
| |

== [[Projects:PointSetRigidRegistration|Point Set Rigid Registration]] ==

In this work, we propose a particle filtering approach for the problem of registering two point sets that differ by
a rigid body transformation. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks. [[Projects:PointSetRigidRegistration|More...]]

'''New: ''' R. Sandhu, S. Dambreville, A. Tannenbaum. Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics. In CVPR, 2008.

|-

| | [[Image:ShapeBasePstSegSlicer.png|200px|]]
| |

== [[Projects:ProstateSegmentation|Prostate Segmentation]] ==

The 3D prostate MRI images are collected by collaborators at Queen’s University. With a little manual initialization, the algorithm provided the results give to the left. The method mainly uses Random Walk algorithm. [[Projects:ProstateSegmentation|More...]]

'''New: ''' Y. Gao, A. Tannenbaum; Shape based MRI prostate image segmentation using local information driven directional distance Bayesian method. SPIE Medical Imaging 2010.

|-

| | [[Image:MultiObjSeg.png|200px|]]
| |

== [[RobustStatisticsSegmentation|Simultaneous Multiple Object Segmentation using Robust Statistics Features ]] ==

Multiple objects are segmented simultaneously using several interactive active contours based on the feature image which utilizes the robust statistics of the image. [[RobustStatisticsSegmentation|More...]]

|-

| | [[Image:ProstateRegSupineToProneInParaview.png|200px|]]
| |

== [[Projects:pfPtSetImgReg|Particle Filter Registration of Medical Imagery]] ==

3D volumetric image registration is performed. The method is based on registering the images through point sets, which is able to handle long distance between as well as registration between Supine and Prone pose prostate. [[Projects:pfPtSetImgReg|More...]]

'''New: ''' Y. Gao, R. Sandhu, G. Fichtinger, A. Tannenbaum; A coupled global registration and segmentation framework with application to magnetic resonance prostate imagery. IEEE TMI (in submission).

|-

| | [[Image:Results brain sag.JPG|200px]]
| |

== [[Projects:OptimalMassTransportRegistration|Optimal Mass Transport Registration]] ==

The goal of this project is to implement a computationaly efficient Elastic/Non-rigid Registration algorithm based on the Monge-Kantorovich theory of optimal mass transport for 3D Medical Imagery. Our technique is based on Multigrid and Multiresolution techniques. This method is particularly useful because it is parameter free and utilizes all of the grayscale data in the image pairs in a symmetric fashion and no landmarks need to be specified for correspondence. [[Projects:OptimalMassTransportRegistration|More...]]

'''New: ''' Eldad Haber, Tauseef Rehman, and Allen Tannenbaum. An Efficient Numerical Method for the Solution of the L2 Optimal Mass Transfer Problem. In submission - SIAM Journal of Scientific Computing, 2009.

'''New: ''' Tauseef Rehman, Eldad Haber, Gallagher Pryor, and Allen Tannenbaum. Fast Optimal Mass Transport for 2D Image Registration and Morphing. Accepted for - Elsevier Journal of Image and Vision Computing, 2009.

|-

| | [[Image:Gatech caudateBands.PNG|200px]]
| |

== [[Projects:MultiscaleShapeSegmentation|Multiscale Shape Segmentation Techniques]] ==

To represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. [[Projects:MultiscaleShapeSegmentation|More...]]

|-

| | [[Image:Caudate Nucleus Denoising.JPG|200px|]]
| |

== [[Projects:WaveletShrinkage|Wavelet Shrinkage for Shape Analysis]] ==

Shape analysis has become a topic of interest in medical imaging since local variations of a shape could carry relevant information about a disease that may affect only a portion of an organ. We developed a novel wavelet-based denoising and compression statistical model for 3D shapes. [[Projects:WaveletShrinkage|More...]]

|-

| | [[Image:Basis membership.png|200px]]
| |

== [[Projects:MultiscaleShapeAnalysis|Multiscale Shape Analysis]] ==

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. [[Projects:MultiscaleShapeAnalysis|More...]]

|-

| | [[Image:Dlpfc1.jpg|200px|]]
| |

== [[Projects:RuleBasedDLPFCSegmentation|Rule-Based DLPFC Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the dorsolateral prefrontal cortex. [[Projects:RuleBasedDLPFCSegmentation|More...]]

|-

| | [[Image:Striatum1.png|200px|]]
| |

== [[Projects:RuleBasedStriatumSegmentation|Rule-Based Striatum Segmentation]] ==

In this work, we provide software to semi-automate the implementation of segmentation procedures based on expert neuroanatomist rules for the striatum. [[Projects:RuleBasedStriatumSegmentation|More...]]

|-

| | [[Image:Brain-flat.PNG|200px]]
| |

== [[Projects:ConformalFlatteningRegistration|Conformal Flattening]] ==

The goal of this project is for better visualizing and computation of neural activity from fMRI brain imagery. Also, with this technique, shapes can be mapped to shperes for shape analysis, registration or other purposes. Our technique is based on conformal mappings which map genus-zero surface: in fmri case cortical or other surfaces, onto a sphere in an angle preserving manner. [[Projects:ConformalFlatteningRegistration|More...]]

|-

| | [[Image:Fig1yan.PNG|200px|]]
| |

== [[Projects:BloodVesselSegmentation|Blood Vessel Segmentation]] ==

The goal of this work is to develop blood vessel segmentation techniques for 3D MRI and CT data. The methods have been applied to coronary arteries and portal veins, with promising results. [[Projects:BloodVesselSegmentation|More...]]

'''New: ''' V.Mohan, G. Sundaramoorthi, A. Stillman and A. Tannenbaum. Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. CI2BM at MICCAI 2009, September 2009.

|-

| | [[Image:Fig67.png|200px|]]
| |

== [[Projects:KnowledgeBasedBayesianSegmentation|Knowledge-Based Bayesian Segmentation]] ==

This ITK filter is a segmentation algorithm that utilizes Bayes's Rule along with an affine-invariant anisotropic smoothing filter. [[Projects:KnowledgeBasedBayesianSegmentation|More...]]

|-

| | [[Image:Stochastic-snake.png|200px|]]
| |

== [[Projects:StochasticMethodsSegmentation|Stochastic Methods for Segmentation]] ==

New stochastic methods for implementing curvature driven flows for various medical tasks such as segmentation. [[Projects:StochasticMethodsSegmentation|More...]]

|-

| | [[Image:GT-SulciOutlining1.jpg|200px]]
| |

== [[Projects:SulciOutlining|Automatic Outlining of sulci on the brain surface]] ==

We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain. [[Projects:SulciOutlining|More...]]

|-

| | [[Image:Table1.png|200px|]]
| |

== [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|KPCA, LLE, KLLE Shape Analysis]] ==

The goal of this work is to study and compare shape learning techniques. The techniques considered are Linear Principal Components Analysis (PCA), Kernel PCA, Locally Linear Embedding (LLE) and Kernel LLE. [[Projects:KPCA_LLE_KLLE_ShapeAnalysis|More...]]

|-

| | [[Image:Gatech SlicerModel2.jpg|200px]]
| |

== [[Projects:StatisticalSegmentationSlicer2|Statistical/PDE Methods using Fast Marching for Segmentation]] ==

This Fast Marching based flow was added to Slicer 2. [[Projects:StatisticalSegmentationSlicer2|More...]]

|}

2010 Winter Project Week SegmentationEndocardialWall

2010-01-08T17:09:05Z

Behnood: Created page with '__NOTOC__ <gallery> Image:PW-SLC2010.png|Projects List Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall Image:…'

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Allen Tannenbaum, Georgia Tech
* Rob MacLeod, Josh Blauer, University of Utah

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Atrial fibrillation (AF), which is caused by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs can be used to control this condition but it involves side effects and only 40-60% of the AF population is maintained in regular rhythm one year after such treatment. An alternative treatment is catheter ablation in which the sources of electrical dyssynchrony are suppressed by ablating specific tissues using a special catheter. Ablation is performed by exposing the tissue to a high temperature, which results in lesion (scar) formation. This procedure, if successful, provides a final cure to AF.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.

More detail and testing are at [[Projects:SegmentationEpicardialWall|here.]]

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We improved the segmentation robustness and performance.
Future works include extending the results to the epicardial wall and image analysis of the left atrial wall.
We have the code in native C++ code and we will transfer it into a suitable type of module in Slicer.

</div>
</div>

<div style="width: 97%; float: left;">

==References==
* Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

</div>

2010 Winter Project Week

2010-01-08T17:08:47Z

Behnood: /* Segmentation */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__
[[Image:PW-SLC2010.png|300px]]

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates_Venue_Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

== Modules and extensions==

* [[Media:3DSlicer-Modules%2BExtensions-2009-11-27.ppt|Overview]]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Requirements_for_Modules Requirements for modules]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Introduction User-side explanations]
* [http://wiki.slicer.org/slicerWiki/index.php/Slicer3:Extensions Developer-side explanations]

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3|Spine Segmentation Module in Slicer3]] (Martin Loepprich, Sylvain Jaume, Polina Golland, Ron Kikinis, Steve Pieper)
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Steve Pieper)
#[[2010_Winter_Project_Week_RobustStatisticsDrivenActiveContourSegmentation|Active contour segmentation using robust statistics]] (Yi Gao, Allen Tannenbaum, GT; Andriy Fedorov, Katie Hayes Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_SegmentationWizard|High Level Wizard for Segmentation of Images]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_LongitudinalLupusAnalyses|Longitudinal Analyses of Lesions in Lupus]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_MultiscaleLupusAnalyses|Multiscale Analyses of Lupus Patients]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_ProstateSeg|Prostate segmentation using shape-based method]] (Andras Lasso, Gabor Fichtinger, Yi Gao, Allen Tannenbaum, Andriy Fedorov)
#[[2010_Winter_Project_Week_TubularTreeSeg|Tubular Tree Segmentation for brain and cardiac imagery]] (Vandana Mohan, Allen Tannenbaum, GT; Marek Kubicki, BWH)
#[[2010_Winter_Project_Week_SegmentationEndocardialWall|Segmentation of the Endocardial Wall]] (Behnood Gholami, Yi Gao, Allen Tannenbaum, GT; Rob MacLeod, Josh Blauer, University of Utah)
#[[2010_Winter_Project_Week_SegmentationMeshEmbeddedContours|Segmentation on Mesh Surfaces Using Geometric Information]] (Peter Karasev, Matias Perez, Allen Tannenbaum, GT; Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_TBISegmentation|Segmentation of TBI (Traumatic Brain Injury) Subjects from Multimodal MRI]] (Marcel Prastawa, Guido Gerig, Ron Kikinis)
#[[2010_Winter_Project_Week_Cardiac_Ablation_Scar_Segmentation|Cadiac Ablation Scar Segmentation]] (Michal Depa, Polina Golland, Ehud Schmidt, Ron Kikinis)
#[[2010_Winter_Project_Week_Musco_Skeletal_Segmentation | Rapid Segmentation of Knee Structures for Simulation]] (Harish Doddi, Saikat Pal, Luis Ibanez, Scott Delp)
#[[2010_Winter_Project_Week_WMLS | White Matter Lesion segmentation]] (Minjeong Kim UNC)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)
#[[2010_Winter_Project_Week_RegistrationInfrastructure|Registration Infrastructure]] (Casey Goodlett, Dominik Meier, Ron Kikinis)
#[[2010_Winter_Project_Week_Deformation_Field_Visualization|Deformation Field and Tensor Visualization]] (Garrett Larson, Martin Styner)
#[[2010_Winter_Project_Week_ThalamicNucleiAtlas | Fusion of Anatomy,MRI and Electrophysiology in Parkinson's]] (Andrzej Przybyszewski, Dominik Meier, Ron Kikinis)
# [[ 2010_Winter_Project_Week_testbed|Testbed for Evaluation, Comparison, and Parameter Exploration for 3D Registration]] (James Fishbaugh, Casey Goodlett, Guido Gerig)
# [[ 2010_Winter_Project_Week_HAMMER|HAMMER Registration Algorithm in Slicer 3]] (Guorong Wu, Xiaodong Tao, Jim Miller, and Dinggang Shen)
# [[ 2010_Winter_Project_Week_Congealing_Testing_Debugging|Congealing ITK Code Testing and Debugging]] (Lauren O'Donnell and Luis Ibanez)

=== IGT ===
#[[Tissue_Dependent_Registration|Registration with Varying Elastic Parameters for Tumor Resection]] (Petter Risholm, Sandy Wells)
#[[2010_Winter_Project_Week_MRI_Reconstruction_by_Registration | MRI Reconstruction by Registration for Focused Ultrasound Therapy]] (Ben Schwartz, Sandy Wells)
# [[2010_Winter_Project_Week_MRI_Guided_Robotic_Prostate_Intervention| MRI-guided Robotic Prostate Intervention]] (Andras Lasso and Junichi Tokuda)
#[[ 2010_Winter_Project_Week_WM_ATLAS|Atlas-Based White Matter Segmentation for Neurosurgical Planning]] (Lauren O'Donnell, C-F Westin, Alexandra J. Golby)
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI|Fast Imaging Library, and Siemens EPI for IGT]] (Scott Hoge, Nick Todd, Dennis Parker, Katie Hayes)

=== Radiotherapy ===
# [[2010_Winter_Project_Week_DicomRT_Plugin|DicomRT plugin for Slicer]] (Greg Sharp, others)
# [[Adaptive Radiotherapy for Head, Neck, and Thorax]] (Ivan Kolesov, Vandana Mohan, Greg Sharp, Allen Tannenbaum )

=== Analysis ===
#[[2010_Winter_Project_Week_VervetMRILongitudinalAnalysis|Vervet MRI Longitudinal Analysis]] (Andriy Fedorov, Ron Rikinis, Ginger Li, Chris Wyatt)
#[[2010_WinterProject_Week_MRSIModule|MRSI Module]] (Bjoern Menze, Polina Golland)
#[[2010_WinterProject_Week_CorticalThicknessAnalysis|Cortical thickness analysis]] (Clement Vachet, Heather Cody Hazlett, Martin Styner)
#[[2010_WinterProject_Week_XNATUseforPopulationAnalysis|XNAT Use for Population Analysis]] (Corentin Hamel, Martin Styner, Clement Vachet)

=== Informatics ===
#[[2010_Winter_Project_Week_XND|XNAT Desktop User Interface]] (Dan M, Wendy P, Ron K)
#[[2010_Winter_Project_Week_Slicer_XNAT|Slicer 3 XNAT Performance Tuning]] (Wendy P, Dan M, Tim Olson, Nicole Aucoin)
#[[2010_Winter_Project_Week_catalyst|Harvard CTSC XNAT]] (Yong Gao, Dan M, Tim Olson, John Paulett)
#[[2010_Winter_Project_Week_xnatfs|xnatfs Integration into XNAT core]] (Dan Blezek, John Paulett, Tim Olsen)
#[[2010_Winter_Project_Week_OAWMB|Open Access Whole body CT/MR data set]] (Dan Marcus, Steve Pieper)
#[[2010_Winter_Project_Week_mComment | Annotation of Medical Images]] (Kilian Pohl, Yong Zhang, Nicole Aucoin, Wendy Plesniak, Steve Pieper)

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin, Casey Goodlett)
#[[ 2010_Winter_Project_Week_HARDI_CONNECTIVITY|Connectivity Study of Neonatal Brain Data using HARDI Techniques]] ( Yundi(Wendy) Shi, Deepika Mahalingam, Martin Styner )
#[[2010_Winter_Project_Week_TractographyPickingEditing|Tractography Picking and Bundle Editing]] (Jim Miller, Mahnaz Maddah, Nicole Aucoin, Wendy Plesniak, James Malcolm, Alex Yarmarkovich)
#[[ 2010_Winter_Project_Week_DTI_Fiber_Tract_Statistics|DTI Fiber-Tract Statistics]] (Anuja Sharma, Guido Gerig)
#[[ 2010_Winter_Project_Week_Tractography_using_DTI_Atlasing|Tractography using DTI Atlasing]] (Gopalkrishna Veni, Ross Whitaker, Sarang Joshi)
#[[2010_Winter_Project_Week_DTI_QualityControl|DTI Quality Control tools integration with NITRC]] (Hans Johnson, UNC)

=== NA-MIC Kit Internals ===
#[[2010_Winter_Project_Week_Testing_for_Extensions|Testing for Extensions]] (Steve, Andriy Fedorov, Jim, Julien Jomier, Katie Hayes, Stuart Wallace)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen, Jim Miller)
#[[2010_Winter_Project_Week_VTK_3D_Widgets_in_Slicer3|VTK 3D Widgets in Slicer3]] (Nicole Aucoin, Karthik, Will)
#[[2010_Winter_Project_Week_Slicer3_Colors_Module|Updates to Slicer3 Colors Module]] (Nicole Aucoin)
#[[2010_Winter_Project_Week_CMAKE_Build_process|CMAKE_Build_process]] (Dave Partyka, Katie Hayes)
#[[2010_Winter_Project_Week_XNAT_Packaging_For_Slicer | Integration of XNAT Packaging for Slicer Internals]] (Dan, Tim Olsen, Steve Pieper, Dave Partyka, Wendy, Randy)
#[[2010_Winter_Project_Week_Orthogonal_Planes_Issues|Orthogonal planes in reformat widget issues in Slicer3.5]] (Michal Depa, Steve Pieper)
#[[2010_Winter_Project_Week_Slicer_Dashboard|Slicer Dashboard]] (Luis, Steve, Bill & All)

(Other possibilities: Plotting, Layouts)

=== NA-MIC Outreach ===
#[[AHM 2010 Tutorial Polishing | Tutorial Polishing]] (Stuart Wallace, Randy Gollub, Sonia Pujol, all contributing tutorial contest developers)

=== Execution Model ===
# [[2010_Winter_Project_Week_Qt-ing the Command Line Module | Qt-ing the Command Line Module]] (Jim Miller, Jean-Christophe Fillion-Robin, Julien Finet)
# [[2010_Winter_Project_Week_Command Line Module Simple Return Types | Simple Return Types]] (Jim Miller)

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

2010 Winter Project Week SegmentationEpicardialWall

2010-01-07T22:49:47Z

Behnood:

2010 Winter Project Week SegmentationEpicardialWall

2010-01-07T22:45:41Z

Behnood:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:3D_Segmentation_LA.png | 3D View of the Segmentation of Endocardial Wall
Image:2d_axial_LA.png | 2D View
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Allen Tannenbaum, Georgia Tech
* Rob MacLeod, Josh Blauer, University of Utah

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Atrial fibrillation (AF), which is caused by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs can be used to control this condition but it involves side effects and only 40-60% of the AF population is maintained in regular rhythm one year after such treatment. An alternative treatment is catheter ablation in which the sources of electrical dyssynchrony are suppressed by ablating specific tissues using a special catheter. Ablation is performed by exposing the tissue to a high temperature, which results in lesion (scar) formation. This procedure, if successful, provides a final cure to AF.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.

More detail and testing are at [[Projects:SegmentationEpicardialWall|here.]]

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have the code in native C++ code and we will transfer it into a suitable type of module in Slicer.

</div>
</div>

<div style="width: 97%; float: left;">

==References==
* Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

</div>

File:2d axial LA.png

2010-01-07T22:44:40Z

Behnood:

2010 Winter Project Week SegmentationEpicardialWall

2010-01-07T22:43:55Z

Behnood:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
Image:3D_Segmentation_LA.png | Segmentation of Endocardial Wall
</gallery>

==Key Investigators==
* Behnood Gholami, Yi Gao, Allen Tannenbaum, Georgia Tech
* Rob MacLeod, Josh Blauer, University of Utah

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Atrial fibrillation (AF), which is caused by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. Electrical cardioversion and antiarrhythmic drugs can be used to control this condition but it involves side effects and only 40-60% of the AF population is maintained in regular rhythm one year after such treatment. An alternative treatment is catheter ablation in which the sources of electrical dyssynchrony are suppressed by ablating specific tissues using a special catheter. Ablation is performed by exposing the tissue to a high temperature, which results in lesion (scar) formation. This procedure, if successful, provides a final cure to AF.

Magnetic resonance imaging (MRI) has been used for both pre- and and post-ablation assessment of the atrial wall. The MRI assessment can aid in selecting the right candidate for the ablation procedure, and also in the assessment of the post-ablation scar formation. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step toward the general solution to the computer-assisted segmentation of the left atrial wall, we propose a new scheme which uses statistical shape learning and segmentation driven by regional statistics to segment the epicardial wall of the left atrium.

More detail and testing are at [[Projects:SegmentationEpicardialWall|here.]]

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Our proposed method is composed of shape registration, shape learning, and image segmentation. Given a training set of binary images corresponding to the segmentations of the epicardial wall of the left atrium, each shape is registered to a fixed arbitrary shape in the training set using a mean-square error registration scheme. Next, we use principal component analysis to learn the shapes. Finally, the shape information available from the learning stage is used to segment the epicardial wall, where the segmentation is driven by local regional statistics. We trained the algorithm on 15 segmentations of the epicardial wall of the left atrium performed by a human expert. The first 6 dominant principal components were used in the statistical shape learning. The algorithm was then used to segment the epicardial wall, as shown in the figure.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have the code in native C++ code and we will transfer it into a suitable type of module in Slicer.

</div>
</div>

<div style="width: 97%; float: left;">

==References==
* Y. Gao, B. Gholami, R. S. MacLeod, J, Blauer, W. M. Haddad, and A. R. Tannenbaum, Segmentation of the Epicardial Wall of the Left Atrium Using Statistical Shape Learning and Local Curve Statistics, SPIE Medical Imaging 2010

</div>

File:3D Segmentation LA.png

2010-01-07T22:42:48Z

Behnood:

2010 Winter Project Week

2010-01-07T22:40:04Z

Behnood: /* Segmentation */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__
[[Image:PW-SLC2010.png|300px]]

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates_Venue_Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

== Modules and extensions==

* [[Media:3DSlicer-Modules%2BExtensions-2009-11-27.ppt|Overview]]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Requirements_for_Modules Requirements for modules]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Introduction User-side explanations]
* [http://wiki.slicer.org/slicerWiki/index.php/Slicer3:Extensions Developer-side explanations]

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3|Spine Segmentation Module in Slicer3]] (Martin Loepprich, Sylvain Jaume, Polina Golland, Ron Kikinis, Steve Pieper)
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Steve Pieper)
#[[2010_Winter_Project_Week_RobustStatisticsDrivenActiveContourSegmentation|Active contour segmentation using robust statistics]] (Yi Gao, Allen Tannenbaum, GT; Andriy Fedorov, Katie Hayes Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_SegmentationWizard|High Level Wizard for Segmentation of Images]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_LongitudinalLupusAnalyses|Longitudinal Analyses of Lesions in Lupus]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_MultiscaleLupusAnalyses|Multiscale Analyses of Lupus Patients]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_ProstateSeg|Prostate segmentation using shape-based method]] (Andras Lasso, Gabor Fichtinger, Yi Gao, Allen Tannenbaum, Andriy Fedorov)
#[[2010_Winter_Project_Week_TubularTreeSeg|Tubular Tree Segmentation for brain and cardiac imagery]] (Vandana Mohan, Allen Tannenbaum, GT; Marek Kubicki, BWH)
#[[2010_Winter_Project_Week_SegmentationEpicardialWall|Segmentation of the Endocardial Wall]] (Behnood Gholami, Yi Gao, Allen Tannenbaum, GT; Rob MacLeod, Josh Blauer, University of Utah)
#[[2010_Winter_Project_Week_SegmentationMeshEmbeddedContours|Segmentation on Mesh Surfaces Using Geometric Information]] (Peter Karasev, Matias Perez, Allen Tannenbaum, GT; Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_TBISegmentation|Segmentation of TBI (Traumatic Brain Injury) Subjects from Multimodal MRI]] (Marcel Prastawa, Guido Gerig, Ron Kikinis)
#[[2010_Winter_Project_Week_Cardiac_Ablation_Scar_Segmentation|Cadiac Ablation Scar Segmentation]] (Michal Depa, Polina Golland, Ehud Schmidt, Ron Kikinis)
#[[2010_Winter_Project_Week_Musco_Skeletal_Segmentation | Rapid Segmentation of Knee Structures for Simulation]] (Harish Doddi, Saikat Pal, Luis Ibanez, Scott Delp)
#[[2010_Winter_Project_Week_WMLS | White Matter Lesion segmentation]] (Minjeong Kim UNC)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)
#[[2010_Winter_Project_Week_RegistrationInfrastructure|Registration Infrastructure]] (Casey Goodlett, Dominik Meier, Ron Kikinis)
#[[2010_Winter_Project_Week_Deformation_Field_Visualization|Deformation Field and Tensor Visualization]] (Garrett Larson, Martin Styner)
#[[2010_Winter_Project_Week_ThalamicNucleiAtlas | Fusion of Anatomy,MRI and Electrophysiology in Parkinson's]] (Andrzej Przybyszewski, Dominik Meier, Ron Kikinis)
# [[ 2010_Winter_Project_Week_testbed|Testbed for Evaluation, Comparison, and Parameter Exploration for 3D Registration]] (James Fishbaugh, Casey Goodlett, Guido Gerig)
# [[ 2010_Winter_Project_Week_HAMMER|HAMMER Registration Algorithm in Slicer 3]] (Guorong Wu, Xiaodong Tao, Jim Miller, and Dinggang Shen)

=== IGT ===
#[[Tissue_Dependent_Registration|Registration with Varying Elastic Parameters for Tumor Resection]] (Petter Risholm, Sandy Wells)
#[[2010_Winter_Project_Week_MRI_Reconstruction_by_Registration | MRI Reconstruction by Registration for Focused Ultrasound Therapy]] (Ben Schwartz, Sandy Wells)
# [[2010_Winter_Project_Week_MRI_Guided_Robotic_Prostate_Intervention| MRI-guided Robotic Prostate Intervention]] (Andras Lasso and Junichi Tokuda)
#[[ 2010_Winter_Project_Week_WM_ATLAS|Atlas-Based White Matter Segmentation for Neurosurgical Planning]] (Lauren O'Donnell, C-F Westin, Alexandra J. Golby)
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI|Fast Imaging Library, and Siemens EPI for IGT]] (Scott Hoge, Nick Todd, Dennis Parker, Katie Hayes)

=== Radiotherapy ===
# [[2010_Winter_Project_Week_DicomRT_Plugin|DicomRT plugin for Slicer]] (Greg Sharp, others)
# [[Adaptive Radiotherapy for Head, Neck, and Thorax]] (Ivan Kolesov, Vandana Mohan, Greg Sharp, Allen Tannenbaum )

=== Analysis ===
#[[2010_Winter_Project_Week_VervetMRILongitudinalAnalysis|Vervet MRI Longitudinal Analysis]] (Andriy Fedorov, Ron Rikinis, Ginger Li, Chris Wyatt)
#[[2010_WinterProject_Week_MRSIModule|MRSI Module]] (Bjoern Menze, Polina Golland)
#[[2010_WinterProject_Week_CorticalThicknessAnalysis|Cortical thickness analysis]] (Clement Vachet, Heather Cody Hazlett, Martin Styner)
#[[2010_WinterProject_Week_XNATUseforPopulationAnalysis|XNAT Use for Population Analysis]] (Corentin Hamel, Martin Styner, Clement Vachet)

=== Informatics ===
#[[2010_Winter_Project_Week_XND|XNAT Desktop User Interface]] (Dan M, Wendy P, Ron K)
#[[2010_Winter_Project_Week_Slicer_XNAT|Slicer 3 XNAT Performance Tuning]] (Wendy P, Dan M, Tim Olson, Nicole Aucoin)
#[[2010_Winter_Project_Week_catalyst|Harvard CTSC XNAT]] (Yong Gao, Dan M, Tim Olson, John Paulett)
#[[2010_Winter_Project_Week_xnatfs|xnatfs Integration into XNAT core]] (Dan Blezek, John Paulett, Tim Olsen)
#[[2010_Winter_Project_Week_OAWMB|Open Access Whole body CT/MR data set]] (Dan Marcus, Steve Pieper)
#[[2010_Winter_Project_Week_mComment | Annotation of Medical Images]] (Kilian Pohl, Yong Zhang, Nicole Aucion, Wendy Plesniak, Steve Pieper)

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin, Casey Goodlett)
#[[ 2010_Winter_Project_Week_HARDI_CONNECTIVITY|Connectivity Study of Neonatal Brain Data using HARDI Techniques]] ( Yundi(Wendy) Shi, Deepika Mahalingam, Martin Styner )
#[[2010_Winter_Project_Week_TractographyPickingEditing|Tractography Picking and Bundle Editing]] (Jim Miller, Mahnaz Maddah, Nicole Aucoin, Wendy Plesniak, James Malcolm, Alex Yarmarkovich)
#[[ 2010_Winter_Project_Week_DTI_Fiber_Tract_Statistics|DTI Fiber-Tract Statistics]] (Anuja Sharma, Guido Gerig)
#[[ 2010_Winter_Project_Week_Tractography_using_DTI_Atlasing|Tractography using DTI Atlasing]] (Gopalkrishna Veni, Ross Whitaker, Sarang Joshi)
#[[2010_Winter_Project_Week_DTI_QualityControl|DTI Quality Control tools integration with NITRC]] (Hans Johnson, UNC)

=== NA-MIC Kit Internals ===
#[[2010_Winter_Project_Week_Testing_for_Extensions|Testing for Extensions]] (Steve, Andriy Fedorov, Jim, Julien Jomier, Katie Hayes, Stuart Wallace)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen, Jim Miller)
#[[2010_Winter_Project_Week_VTK_3D_Widgets_in_Slicer3|VTK 3D Widgets in Slicer3]] (Nicole Aucoin, Karthik, Will)
#[[2010_Winter_Project_Week_Slicer3_Colors_Module|Updates to Slicer3 Colors Module]] (Nicole Aucoin)
#[[2010_Winter_Project_Week_CMAKE_Build_process|CMAKE_Build_process]] (Dave Partyka, Katie Hayes)
#[[2010_Winter_Project_Week_XNAT_Packaging_For_Slicer | Integration of XNAT Packaging for Slicer Internals]] (Dan, Tim Olsen, Steve Pieper, Dave Partyka, Wendy, Randy)
#[[2010_Winter_Project_Week_Orthogonal_Planes_Issues|Orthogonal planes in reformat widget issues in Slicer3.5]] (Michal Depa, Steve Pieper)
#[[2010_Winter_Project_Week_Slicer_Dashboard|Slicer Dashboard]] (Luis, Steve, Bill & All)

(Other possibilities: Plotting, Layouts)

=== NA-MIC Outreach ===
#[[AHM 2010 Tutorial Polishing | Tutorial Polishing]] (Stuart Wallace, Randy Gollub, Sonia Pujol, all contributing tutorial contest developers)

=== Execution Model ===
# [[2010_Winter_Project_Week_Qt-ing the Command Line Module | Qt-ing the Command Line Module]] (Jim Miller, Jean-Christophe Fillion-Robin, Julien Finet)
# [[2010_Winter_Project_Week_Command Line Module Simple Return Types | Simple Return Types]] (Jim Miller)

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

2010 Winter Project Week

2010-01-07T22:39:24Z

Behnood: /* Segmentation */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__
[[Image:PW-SLC2010.png|300px]]

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates_Venue_Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

== Modules and extensions==

* [[Media:3DSlicer-Modules%2BExtensions-2009-11-27.ppt|Overview]]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Requirements_for_Modules Requirements for modules]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Introduction User-side explanations]
* [http://wiki.slicer.org/slicerWiki/index.php/Slicer3:Extensions Developer-side explanations]

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3|Spine Segmentation Module in Slicer3]] (Martin Loepprich, Sylvain Jaume, Polina Golland, Ron Kikinis, Steve Pieper)
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Steve Pieper)
#[[2010_Winter_Project_Week_RobustStatisticsDrivenActiveContourSegmentation|Active contour segmentation using robust statistics]] (Yi Gao, Allen Tannenbaum, GT; Andriy Fedorov, Katie Hayes Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_SegmentationWizard|High Level Wizard for Segmentation of Images]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_LongitudinalLupusAnalyses|Longitudinal Analyses of Lesions in Lupus]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_MultiscaleLupusAnalyses|Multiscale Analyses of Lupus Patients]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_ProstateSeg|Prostate segmentation using shape-based method]] (Andras Lasso, Gabor Fichtinger, Yi Gao, Allen Tannenbaum, Andriy Fedorov)
#[[2010_Winter_Project_Week_TubularTreeSeg|Tubular Tree Segmentation for brain and cardiac imagery]] (Vandana Mohan, Allen Tannenbaum, GT; Marek Kubicki, BWH)
#[[2010_Winter_Project_Week_SegmentationEndocardialWall|Segmentation of the Endocardial Wall]] (Behnood Gholami, Yi Gao, Allen Tannenbaum, GT; Rob MacLeod, Josh Blauer, University of Utah)
#[[2010_Winter_Project_Week_SegmentationMeshEmbeddedContours|Segmentation on Mesh Surfaces Using Geometric Information]] (Peter Karasev, Matias Perez, Allen Tannenbaum, GT; Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_TBISegmentation|Segmentation of TBI (Traumatic Brain Injury) Subjects from Multimodal MRI]] (Marcel Prastawa, Guido Gerig, Ron Kikinis)
#[[2010_Winter_Project_Week_Cardiac_Ablation_Scar_Segmentation|Cadiac Ablation Scar Segmentation]] (Michal Depa, Polina Golland, Ehud Schmidt, Ron Kikinis)
#[[2010_Winter_Project_Week_Musco_Skeletal_Segmentation | Rapid Segmentation of Knee Structures for Simulation]] (Harish Doddi, Saikat Pal, Luis Ibanez, Scott Delp)
#[[2010_Winter_Project_Week_WMLS | White Matter Lesion segmentation]] (Minjeong Kim UNC)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)
#[[2010_Winter_Project_Week_RegistrationInfrastructure|Registration Infrastructure]] (Casey Goodlett, Dominik Meier, Ron Kikinis)
#[[2010_Winter_Project_Week_Deformation_Field_Visualization|Deformation Field and Tensor Visualization]] (Garrett Larson, Martin Styner)
#[[2010_Winter_Project_Week_ThalamicNucleiAtlas | Fusion of Anatomy,MRI and Electrophysiology in Parkinson's]] (Andrzej Przybyszewski, Dominik Meier, Ron Kikinis)
# [[ 2010_Winter_Project_Week_testbed|Testbed for Evaluation, Comparison, and Parameter Exploration for 3D Registration]] (James Fishbaugh, Casey Goodlett, Guido Gerig)
# [[ 2010_Winter_Project_Week_HAMMER|HAMMER Registration Algorithm in Slicer 3]] (Guorong Wu, Xiaodong Tao, Jim Miller, and Dinggang Shen)

=== IGT ===
#[[Tissue_Dependent_Registration|Registration with Varying Elastic Parameters for Tumor Resection]] (Petter Risholm, Sandy Wells)
#[[2010_Winter_Project_Week_MRI_Reconstruction_by_Registration | MRI Reconstruction by Registration for Focused Ultrasound Therapy]] (Ben Schwartz, Sandy Wells)
# [[2010_Winter_Project_Week_MRI_Guided_Robotic_Prostate_Intervention| MRI-guided Robotic Prostate Intervention]] (Andras Lasso and Junichi Tokuda)
#[[ 2010_Winter_Project_Week_WM_ATLAS|Atlas-Based White Matter Segmentation for Neurosurgical Planning]] (Lauren O'Donnell, C-F Westin, Alexandra J. Golby)
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI|Fast Imaging Library, and Siemens EPI for IGT]] (Scott Hoge, Nick Todd, Dennis Parker, Katie Hayes)

=== Radiotherapy ===
# [[2010_Winter_Project_Week_DicomRT_Plugin|DicomRT plugin for Slicer]] (Greg Sharp, others)
# [[Adaptive Radiotherapy for Head, Neck, and Thorax]] (Ivan Kolesov, Vandana Mohan, Greg Sharp, Allen Tannenbaum )

=== Analysis ===
#[[2010_Winter_Project_Week_VervetMRILongitudinalAnalysis|Vervet MRI Longitudinal Analysis]] (Andriy Fedorov, Ron Rikinis, Ginger Li, Chris Wyatt)
#[[2010_WinterProject_Week_MRSIModule|MRSI Module]] (Bjoern Menze, Polina Golland)
#[[2010_WinterProject_Week_CorticalThicknessAnalysis|Cortical thickness analysis]] (Clement Vachet, Heather Cody Hazlett, Martin Styner)
#[[2010_WinterProject_Week_XNATUseforPopulationAnalysis|XNAT Use for Population Analysis]] (Corentin Hamel, Martin Styner, Clement Vachet)

=== Informatics ===
#[[2010_Winter_Project_Week_XND|XNAT Desktop User Interface]] (Dan M, Wendy P, Ron K)
#[[2010_Winter_Project_Week_Slicer_XNAT|Slicer 3 XNAT Performance Tuning]] (Wendy P, Dan M, Tim Olson, Nicole Aucoin)
#[[2010_Winter_Project_Week_catalyst|Harvard CTSC XNAT]] (Yong Gao, Dan M, Tim Olson, John Paulett)
#[[2010_Winter_Project_Week_xnatfs|xnatfs Integration into XNAT core]] (Dan Blezek, John Paulett, Tim Olsen)
#[[2010_Winter_Project_Week_OAWMB|Open Access Whole body CT/MR data set]] (Dan Marcus, Steve Pieper)
#[[2010_Winter_Project_Week_mComment | Annotation of Medical Images]] (Kilian Pohl, Yong Zhang, Nicole Aucion, Wendy Plesniak, Steve Pieper)

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin, Casey Goodlett)
#[[ 2010_Winter_Project_Week_HARDI_CONNECTIVITY|Connectivity Study of Neonatal Brain Data using HARDI Techniques]] ( Yundi(Wendy) Shi, Deepika Mahalingam, Martin Styner )
#[[2010_Winter_Project_Week_TractographyPickingEditing|Tractography Picking and Bundle Editing]] (Jim Miller, Mahnaz Maddah, Nicole Aucoin, Wendy Plesniak, James Malcolm, Alex Yarmarkovich)
#[[ 2010_Winter_Project_Week_DTI_Fiber_Tract_Statistics|DTI Fiber-Tract Statistics]] (Anuja Sharma, Guido Gerig)
#[[ 2010_Winter_Project_Week_Tractography_using_DTI_Atlasing|Tractography using DTI Atlasing]] (Gopalkrishna Veni, Ross Whitaker, Sarang Joshi)
#[[2010_Winter_Project_Week_DTI_QualityControl|DTI Quality Control tools integration with NITRC]] (Hans Johnson, UNC)

=== NA-MIC Kit Internals ===
#[[2010_Winter_Project_Week_Testing_for_Extensions|Testing for Extensions]] (Steve, Andriy Fedorov, Jim, Julien Jomier, Katie Hayes, Stuart Wallace)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen, Jim Miller)
#[[2010_Winter_Project_Week_VTK_3D_Widgets_in_Slicer3|VTK 3D Widgets in Slicer3]] (Nicole Aucoin, Karthik, Will)
#[[2010_Winter_Project_Week_Slicer3_Colors_Module|Updates to Slicer3 Colors Module]] (Nicole Aucoin)
#[[2010_Winter_Project_Week_CMAKE_Build_process|CMAKE_Build_process]] (Dave Partyka, Katie Hayes)
#[[2010_Winter_Project_Week_XNAT_Packaging_For_Slicer | Integration of XNAT Packaging for Slicer Internals]] (Dan, Tim Olsen, Steve Pieper, Dave Partyka, Wendy, Randy)
#[[2010_Winter_Project_Week_Orthogonal_Planes_Issues|Orthogonal planes in reformat widget issues in Slicer3.5]] (Michal Depa, Steve Pieper)
#[[2010_Winter_Project_Week_Slicer_Dashboard|Slicer Dashboard]] (Luis, Steve, Bill & All)

(Other possibilities: Plotting, Layouts)

=== NA-MIC Outreach ===
#[[AHM 2010 Tutorial Polishing | Tutorial Polishing]] (Stuart Wallace, Randy Gollub, Sonia Pujol, all contributing tutorial contest developers)

=== Execution Model ===
# [[2010_Winter_Project_Week_Qt-ing the Command Line Module | Qt-ing the Command Line Module]] (Jim Miller, Jean-Christophe Fillion-Robin, Julien Finet)
# [[2010_Winter_Project_Week_Command Line Module Simple Return Types | Simple Return Types]] (Jim Miller)

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...