NAMIC Wiki - User contributions [en]

2014 Summer Project Week:Pipeline Visualization

2014-06-27T13:53:39Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
Image:atlas_pipeline.png|BRAINSTools Template pipeline
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==
We have developed an extension to nipype (http://nipy.sourceforge.net/nipype/) that allows for visualization of pipelines and their results.
At the project week, we plan to improve the visualization and features and release our code.

Live demo (temporary link): http://shoutkey.com/grant

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We plan to show our demo and get feedback to improve the visualization.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Release code / integrate with nipype codebase
* Discuss wishlist for visualization (come see me if you have any thoughts!)
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Code on github: https://github.com/rameshvs/nipype/tree/visualization-serverhttps://github.com/rameshvs/nipype/tree/visualization-server
* Worked with Dave to visualize Iowa BRAINS pipeline (see screenshot)
</div>
</div>

2014 Summer Project Week:Pipeline Visualization

2014-06-26T18:49:10Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==
We have developed an extension to nipype (http://nipy.sourceforge.net/nipype/) that allows for visualization of pipelines and their results.
At the project week, we plan to improve the visualization and features and release our code.

Live demo (temporary link): http://shoutkey.com/grant

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We plan to show our demo and get feedback to improve the visualization. Additionally, if time permits, we want to start work on combining this with our lightweight interface to nipype for prototyping pipelines.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Release code / integrate with nipype codebase
* Discuss wishlist for visualization (come see me if you have any thoughts!)
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

2014 Summer Project Week:Pipeline Visualization

2014-06-23T18:37:32Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==
We have developed an extension to nipype (http://nipy.sourceforge.net/nipype/) that allows for visualization of pipelines and their results.
At the project week, we plan to improve the visualization and features and release our code.

Live demo (temporary link): http://shoutkey.com/underline

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We plan to show our demo and get feedback to improve the visualization. Additionally, if time permits, we want to start work on combining this with our lightweight interface to nipype for prototyping pipelines.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Release code / integrate with nipype codebase
* Discuss wishlist for visualization (come see me if you have any thoughts!)
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

2014 Summer Project Week:Pipeline Visualization

2014-06-23T18:26:04Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==
We have developed an extension to nipype (http://nipy.sourceforge.net/nipype/) that allows for visualization of pipelines and their results.
At the project week, we plan to improve the visualization and features and release our code.

http://shoutkey.com/underline

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We plan to show our demo and get feedback to improve the visualization. Additionally, if time permits, we want to start work on combining this with our lightweight interface to nipype for prototyping pipelines.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Release code / integrate with nipype codebase
* Discuss wishlist for visualization (come see me if you have any thoughts!)
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

2014 Summer Project Week:Pipeline Visualization

2014-06-23T17:40:07Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We have developed an extension to nipype (http://nipy.sourceforge.net/nipype/) that allows for visualization of pipelines and their results.
At the project week, we plan to improve the visualization and features and release our code.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Release code / integrate with nipype codebase
* Discuss wishlist for visualization (come see me if you have any thoughts!)
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

2014 Summer Project Week

2014-06-23T15:51:27Z

Rameshvs:

__NOTOC__

[[image:PW-MIT2014.png|300px]]

Dates: June 23-27, 2014.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 23
!style="width:18%" |Tuesday, June 24
!style="width:18%" |Wednesday, June 25
!style="width:18%" |Thursday, June 26
!style="width:18%" |Friday, June 27
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-11:30am''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: DICOM|DICOM]] (Steve Pieper)
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|
'''9:00-10:30am''' [[2014_Tutorial_Contest|Tutorial Contest Presentations (Sonia Pujol)]] <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''11am-12noon''' Breakout Session: [[2014_Project_Week_Breakout_Session: Slicer for users| Slicer for users]] (Ron Kikinis)
<br>
|'''10am-12pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: IGT Neuro|Image-Guided Therapy - Neurosurgery]] (Alexandra Golby, Tina Kapur) <br>
[[MIT_Project_Week_Rooms#Star|Star]]

<br>----------------------------------------<br>
'''12pm''' [[Events:TutorialContestJune2014|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' [[2014 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: QIICR|QIICR]] (Andrey Fedorov)
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
|'''1-2:30pm:''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: Contours|Contours]] (Adam Rankin, Csaba Pinter)
[[MIT_Project_Week_Rooms#Kiva|Kiva]]
|'''1-3pm:''' <font color="#503020">Breakout Session:'''</font><br>[[2014 Project Week Breakout Session: IGT Prostate|Image-Guided Therapy - Prostate Interventions]] (Clare Tempany, Noby Hata)
[[MIT_Project_Week_Rooms#Star|Star]]
<br>----------------------------------------<br>
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Background''' ==

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

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

Please make sure that you are on the [http://public.kitware.com/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]

=Projects=
* [[2014_Project_Week_Template | Template for project pages]]

==Image-Guided Therapy==

* [[2014_Summer_Project_Week:SlicerIGT|SlicerIGT extension: testing, tutorials, website]] (Tamas Ungi, Nobuhiko Hata)
*[[2014_Summer_Project_Week:MR-Ultrasound_Registration_for_Prostate_Interventions | MR-Ultrasound Registration for Prostate Interventions]] (Chenxi Zhang, Andriy Fedorov, Andras Lasso)
*[[2014_Summer_Project_Week:Surface_approximation_from_contour_points | Surface approximation from contour points]] (Chenxi Zhang, Csaba Pinter, Andrey Fedorov)
* [[2014_Summer_Project_Week:Robot_Control_With_OpenIGTLink | Robot Control With OpenIGTLink]] ( Gregory Fischer WPI, Nirav Patel WPI, Nobuhiko Hata BWH)
* [[Gestural Point of Care Interface for IGT]] (Saskia, Franklin, Steve, Tobias)
* [[2014_Summer_Project_Week:Intelligent_Steering | Steered image registration using intelligent interfaces for minimal user interaction]] (Marcel Prastawa, Jim Miller, Steve Pieper)
* [[2014_Summer_Project_Week:Image To Mesh Conversion for Brain MRI | Image To Mesh Conversion for Brain MRI]] (Fotis Drakopoulos, Yixun Liu, Andrey Fedorov, Ron Kikinis, Nikos Chrisochoides)
* [[2014_Summer_Project_Week:An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift | An ITK implementation of Physics-Based Non-Rigid Registration method for Brain Shift]] (Fotis Drakopoulos, Yixun Liu, Andriy Kot, Andrey Fedorov, Olivier Clatz, Ron Kikinis, Nikos Chrisochoides)
* [[2014_Summer_Project_Week:Open_source_electromagnetic_trackers_usingOpenIGTLink| Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)
*[[2014_Summer_Project_Week:Intraoperative_Registration_of_preoperative_CT_and_C-arm_CT_of_the_lung | Intraoperative Registration of preoperative CT and C-arm CT of the lung]] (Katharina Breininger, Jay Jagadeesan)
*[[2014_Summer_Project_Week:Image guided neuroendoscope | Making realistic clinical story board for image guided skull base endoscopic surgery]] (Keryn Palmer, Nobuhiko Hata)
*[[2014_Summer_Project_Week:PathExplorer_Extension | PathExplorer Extension (code refactoring, documentation, tutorial)]] (Laurent Chauvin, Tamas Ungi, Nobuhiko Hata)
*[[2014_Summer_Project_Week:Cortical_Dysplasia_Identification | Tools for Dysplasia Identification in Epilepsy]] (Luiz Murta; Emylin Souza; Tina Kapur; Ron Kikinis)

==Huntington's Disease==
*[[2014_Summer_Project_Week:FiberTractDispersion| Fiber Tract Dispersion and UKF Tractography]] (Peter Savadjiev, Yogesh Rathi, Hans Johnson, C-F Westin)

==TBI==
*[[2014_Summer_Project_Week:TBI_Segmentation| Interactive segmentation for traumatic brain injury ]] (Bo Wang, Marcel Prastawa, Andrei Irimia, John D. Van Horn, Guido Gerig)

==Stroke==
*[[2014_Summer_Project_Week:Stroke-ImagingGenetics | Stroke Imaging Genetics]] (Adrian Dalca, Ramesh Sridharan, Polina Golland)
*[[2014_Summer_Project_Week:Stroke-SuperResolution | Stroke Super Resolution]] (Adrian Dalca, Ramesh Sridharan, Polina Golland)

==Chronic Obstructive Pulmonary Disease, Lung, Chest ==

*[[2014_Summer_Project_Week: Pectoralis muscle segmentation| Pectoralis muscle segmentation]] (Rola Harmouche, James Ross, Raul San Jose)
*[[2014_Summer_Project_Week:Image_Registration_with_Sliding_Motion_Constraints | Image Registration with Sliding Motion Constraints]] (Alexander Derksen, Kanglin Chen, Gregory Sharp)
*[[2014_Summer_Project_Week:Multiscale_Non_Local_Means_filter_(NLM)_for_chest_CT_images | Multiscale Non Local Means filter (NLM) for chest CT images]] (Pietro Nardelli, Raul San Jose)

==Head and Neck Cancer / Radiotherapy ==
*[[2014_Summer_Project_Week:External Beam Planning| External Beam Planning]] (Kevin Wang, Greg Sharp, Maxime Desplanques)
*[[2014_Summer_Project_Week:DIR_validation_tools| DIR validation tools]] (Greg Sharp, Ivan Kolesov, Allen Tannenbaum)
*[[2014_Summer_Project_Week:Upload_HN_data| Upload H&N data]] (Greg Sharp, Paolo Zaffino)
*[[2014_Summer_Project_Week:DIR_stop_and_restart| DIR stop and restart]] (Paolo Zaffino, Greg Sharp, Steve Pieper)
*[[2014_Summer_Project_Week:InteractiveRegistration| Interactive Registration]] (Ivan Kolesov, Greg Sharp, Allen Tannenbaum)
*[[2014_Summer_Project_Week:Proton_pencil_beam| Proton pencil beam dose calculation]] (Maxime Desplanques, Kevin Wang, Greg Sharp)

==[http://qiicr.org QIICR]==
* [[2014_Summer_Project_Week: RWV mapping support|Real world value mapping support]] (Andrey, Ethan, Andras, Steve, Jim)
* [[2014_Summer_Project_Week: CLI Derived DICOM Data| Proper formatting of DICOM Derived Data from CLI]] (Steve, Andrey, Jim, {Michael and David remotely})
* [[2014_Summer_Project_Week: DICOM SEG conversion to support archival of QIN Grand challenges results|DICOM SEG conversion to support archival of QIN Grand challenges results]] (Jayashree, Andrey, Steve, {David remotely})
* [[2014_Summer_Project_Week: ColorBar support|Color Bar Support for Slice Views]] (Alireza, Andrey, Steve, Kevin)
* [[2014_Summer_Project_Week: Slicer DICOM|Slicer DICOM Improvements]] (Alireza, Andrey, Steve, Ron)

==Feature Extraction==
*[[2014_Summer_Project_Week:Tumor_DCE-MRI_Segmentation | Breast Tumor Segmentation]] (Vivek Narayan, Jay Jagadeesan)
*[[2014_Summer_Project_Week:Tumor_Heterogeneity_Analysis | Breast Tumor Heterogeneity Analysis]] (Vivek Narayan, Jay Jagadeesan)
*Quantitative image feature extraction in Non-Small Cell Lung Cancer (Hugo Aerts)
*[[2014_Summer_Project_Week:Invariant_Feature_Extraction_Slicer | Invariant Feature Methods in Slicer]] (Matthew Toews, Nicole Aucoin, Sandy Wells)

==Additional Brain Image Analysis==
*[[2014_Summer_Project_Week:Slicer_Murin_Shape_Analysis | Shape Analysis for the developing murine skull]] (Murat Maga, Ryan Young, Seattle Chidren's Hospital).
*[[2014_Summer_Project_Week:Slicer_LDDMM_Shape_Analysis | Slicer Interface to LDDMM shape anlaysis]] (Saurabh Jain, JHU; Steve Pieper, Isomics; Josh Cates, SCI, Utah; Hans Johnson, Iowa; Martin Styner, UNC)
*[[2014_Summer_Project_Week:Atlas Selection | Atlas Selection]] (Kanglin Chen, Gregory Sharp)
*[[2014_Summer_Project_Week:CAD_Toolbox_for_Neurological_Disorders | CAD Toolbox for Neurological Disorders]] (Sidong Liu, Siqi Liu, Fan Zhang, Yang Song, Weidong Cai, Sonia Pujol, Ron Kikinis)
*[[2014_Summer_Project_Week:Longitudinal_patient_specific_DTI_analysis | Longitudinal patient-specific DTI analysis using Slicer for neonatal asphyxia]] (Anuja Sharma, SCI, Utah; Francois Budin, UNC; Martin Styner, UNC; Guido Gerig, SCI, Utah)
*[[2014_Summer_Project_Week:mipiX | Rapid Visualization of Large Image Collections]] (Adrian, Ramesh, Polina)
*[[2014_Summer_Project_Week:Pipeline_Visualization | Pipeline Visualization]] (Ramesh, Adrian, Polina)
==Slicer4 Extensions==

*[[2014_Summer_Project_Week:Multidim Data| Multidimensional Data]] (Andras Lasso, Kevin Wang)
*[[2014_Summer_Project_Week:DICOM-SRO import| DICOM-SRO import]] (Kevin Wang)
*[[2014_Summer_Project_Week:PLM_engineering| Plastimatch extension re-engineering]] (Greg Sharp, Paolo Zaffino, Andras, Csaba, Kevin)
*[[2014_Summer_Project_Week:DRAMMS_Slicer| Integrating DRAMMS deformable registration into Slicer]] (Yangming Ou, Steve Pieper, Andriy Fedorov, Tina Kapur, Christos Davatzikos, Ron Kikinis, Randy Gollub, Jayashree Kalpathy-Cramer)

==Infrastructure==
*Slicer 4.4 Release (JC, Steve, Nicole)
* [[2014_Summer_Project_Week: Chronicle| Chronicle]] (Steve)
* [[2014_Summer_Project_Week: Factory and Testing Process Post NA-MIC| Post NA-MIC Factory and Testing]] (Steve, Jc, Ron)
* [[2014_Summer_Project_Week: Volume Registration|Volume Registration]] (Steve, Greg, Marcel, Jim)
* [[2014_Summer_Project_Week:Markups | Markups]] (Nicole Aucoin)
*[[2014_Summer_Project_Week:Pluggable Label Statistics |Pluggable Label Statistics]] (Andrey , Ethan, Steve, Brad, Jim)
*[[2014_Summer_Project_Week:Subject_hierarchy_integration | Subject hierarchy integration]] (Csaba, Steve, Jc, Andras)
*[[2014_Summer_Project_Week:Contours | Contours]] (Adam Rankin, Csaba, Andras, Steve, Jc)
*[[2014_Summer_Project_Week:Parameter Node Serialization | Parameter Node Serialization]] (Kevin Wang, Andras, Steve, Jim, Csaba)
*[[2014_Summer_Project_Week:Self-tests for non-linear transforms | Self-tests for non-linear transforms]] (Xining Du)
*[[2014_Summer_Project_Week:Slicer Tutorial Updates | Slicer Tutorial Updates]] (Parth Amin, Farukh Kohistani)

== '''Logistics''' ==

*'''Dates:''' June 23-27, 2014.
*'''Location:''' [[MIT_Project_Week_Rooms| Stata Center / RLE MIT]].
*'''REGISTRATION:''' https://www.regonline.com/namic2014summerprojectweek. Please note that as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on "No thanks" in order to finish your Project Week registration.
*'''Registration Fee:''' $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list: [[2014_Summer_Project_Week/RoomSharing|here]]

== '''Registrants''' ==

Do not add your name to this list - it is maintained by the organizers based on your paid registration. ([https://www.regonline.com/namic2014summerprojectweek Please click here to register.])

#Hugo Aerts, Dana Farber/Harvard, hugo_aerts@dfci.harvard.edu
#Nassim Alikacem, Brigham & Women's Hospital, Nassim.Alikacem@gmail.com
#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, Brigham & Women's Hospital, nicole@bwh.harvard.edu
#Eva Breininger, Brigham & Women's Hospital, ebreininger@partners.org
#Francois Budin, NIRAL-UNC, fbudin@unc.edu
#Saskia Camps, SPL, saskiacamps@gmail.com
#Lucia Cevidanes, University of Michigan, luciacev@umich.edu
#Laurent Chauvin, SPL, lchauvin@bwh.harvard.edu
#Kanglin Chen, Fraunhofer MEVIS, kanglin.chen@mevis.fraunhofer.de
#Adrian Dalca, MIT CSAIL, adalca@mit.edu
#Alexander Derksen, Fraunhofer MEVIS, alexander.derksen@mevis.fraunhofer.de
#Maxime Desplanques, MGH/Politecnico di Milano, maxime.desplanques@cnao.it
#Fotis Drakopoulos, Old Dominion University, fdrakopo@gmail.com
#Sneha Durgapal, Brigham & Women's Hospital, durgapalsneha@gmail.com
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#James Fishbaugh, SCI Institute/University of Utah, jfishbaugh@gmail.com
#Jessica Forbes, University of Iowa, jessica-forbes@uiowa.edu
#Polina Golland, MIT CSAIL, polina@csail.mit.edu
#Jeffrey Grethe, University of CA San Diego, jgrethe@ncmir.ucsd.edu
#Rola Harmouche, Brigham & Women's Hospital, rolaharmouche@gmail.com
#Nobuhiko Hata, Brigham & Women's Hospital, hata@bwh.harvard.edu
#Saurabh Jain, Johns Hopkins University, saurabh@cis.jhu.edu
#Hans Johnson, University of Iowa, hans-johnson@uiowa.edu
#Jayashree Kalpathy-Cramer, MGH, kalpathy@nmr.mgh.harvard.edu
#Tina Kapur, BWH/Harvard Medical School, tkapur@bwh.harvard.edu
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Regina Kim, University of Iowa, eunyoung-kim@uiowa.edu
#Franklin King, Queen's University, franklin.king@queensu.ca
#Tassilo Klein, SPL/BWH, TJKlein@bwh.harvard.edu
#Farukh Kohistani, BWH Radiology, kohistan@bc.edu
#Robin Kouver, BWH/SPL, r.kouver@gmail.com
#Andreas Lasso, PerkLab - Queen's University, lasso@queensu.ca
#Yangming Li, University of Washington, ymli81@uw.edu
#Sidong Liu, SPL/BWH, sliu@bwh.harvard.edu
#Siqi Liu, University of Sydney, sliu4512@uni.sydney.edu.au
#Bradley Lowekamp, National Institutes of Health, blowekamp@mail.nih.gov
#Murat Maga, Seattle Children's Research Institute, maga@uw.edu
#Katie Mastrogiacomo, SPL/BWH, kmast@bwh.harvard.edu
#Alireza Mehrtash, SPL/BWH, mehrtash@bwh.harvard.edu
#Dominik Meier, Brigham & Women's Hospital, meier@bwh.harvard.edu
#Jim Miller, GE Research, millerjv@ge.com
#Luiz Otavio Murta Junor, SPL/BWH, lmurta@partners.org
#Vivek Narayan, NCIGT, narayan.vivek9@gmail.com
#Pietro Nardelli, University College Cork, pietro@bwh.harvard.edu
#Yangming Ou, MGH, yangming.ou@uphs.upenn.edu
#Danielle Pace, MIT CSAIL, dfpace@mit.edu
#Keryn Palmer, Brigham & Women's Hospital, kpalmer5@partners.org
#Nirav Patel, WPI, napatel@wpi.edu
#Tobias Penzkofer, SPL, pt@bwh.harvard.edu
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's University, csaba.pinter@queensu.ca
#Marcel Prastawa, GE Research, marcel.prastawa@ge.com
#Somia Pujol, Harvard Medical School, spujol@bwh.harvard.edu
#Adam Rankin, Queen's University, rankin@queensu.ca
#Aymeric Reshef, Brigham & Women's Hospital, areshef@bwh.harvard.edu
#Rahul Sastry, BWH/SPL, rahul_sastry@hms.harvard.edu
#Peter Savadjiev, Brigham & Women's Hospital, petersv@bwh.harvard.edu
#Gregory Sharp, MGH, gcsharp@mgh.harvard.edu
#Emylin Sousa, BWH/SPL, emylin.sousa@gmail.com
#Ramesh Sridharan, MIT CSAIL, rameshvs@csail.mit.edu
#Matthew Toews, BWH/Harvard Medical School, mt@bwh.harvard.edu
#Ethan Ulrich, University of Iowa, ethan-ulrich@uiowa.edu
#Tamas Ungi, Queen's University, ungi@queensu.ca
#Kevin Wang, Princess Margaret Cancer Centre, kevin.wang@rmp.uhn.ca
#David Welch, University of Iowa, david-welch@uiowa.edu
#William Wells, Brigham & Women's Hospital, sw@bwh.harvard.edu
#Phillip White, BWH/Harvard Medical School, white@bwh.harvard.edu
#Alex Yarmarkovich, ISOMICS Inc., alexy@bwh.harvard.edu
#Ryan Young, Seattle Children's Research Institute, ryan.young@seattlechildrens.org
#Paolo Zaffino, University Magna Graecia of Catanzaro, p.zaffino@unicz.it
#Chenxi Zhang, Brigham & Women's Hospital, chenxizhang@fudan.edu.cn
#Fan Zhang, University of Sydney, fzha8048@uni.sydney.edu.au

2014 Summer Project Week:Pipeline Visualization

2014-06-23T15:50:20Z

Rameshvs: Created page with '__NOTOC__ <gallery> Image:PW-MIT2014.png|Projects List Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code </gal…'

__NOTOC__
<gallery>
Image:PW-MIT2014.png|[[2014_Summer_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We have developed an extension to nipype (http://nipy.sourceforge.net/nipype/) that allows for visualization of pipelines and their results.
At the project week, we plan to improve the visualization and features and release our code.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Integrate with nipype codebase
* Discuss wishlist for visualization (come see me if you have any thoughts!)
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

2014 Project Week:Processing Pipelines

2014-01-10T16:01:35Z

Rameshvs: /* Project Description */

__NOTOC__

<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Golland, MIT

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We have developed simple, very lightweight software for building processing pipelines: <br />
https://www.github.com/rameshvs/medical-imaging-pipelines <br />
At the project week, we plan to finish building basic visualization of pipelines and a framework for making pipeline output easily visualizable in tools like Slicer. <br />
This system is meant to be a more lightweight alternative to existing tools such as nipype (http://nipy.org/nipype/): nipype provides a broad range of features but requires learning the intricacies of its system. Our system is meant to provide the core subset of those features, but in turn will be much simpler to learn, use, and work with.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Implement visualization (see screenshot)
* Discuss wishlist for pipeline (come see me if you have any thoughts!)
* Discuss MRML/MRB output for visualizing results in Slicer
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Built better visualization (see screenshot)
* Had many productive discussions with ideas for pipeline
* Started data provenance tracking system and partial script execution
</div>
</div>

File:Pipeline visualization sketch.png

2014-01-10T15:51:09Z

Rameshvs: uploaded a new version of "File:Pipeline visualization sketch.png": Reverted to version as of 15:50, 10 January 2014

Preliminary version of visualized processing pipeline

File:Pipeline visualization sketch.png

2014-01-10T15:50:57Z

Rameshvs: uploaded a new version of "File:Pipeline visualization sketch.png": Reverted to version as of 19:03, 6 January 2014

Preliminary version of visualized processing pipeline

File:Pipeline visualization sketch.png

2014-01-10T15:50:39Z

Rameshvs: uploaded a new version of "File:Pipeline visualization sketch.png": Improved readability, viewability

Preliminary version of visualized processing pipeline

2014 Project Week:Multi-Tissue Stroke Segmentation

2014-01-10T00:58:16Z

Rameshvs:

__NOTOC__

<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:WMH_T1.png‎| T1 images in stroke dataset.
Image:WMHseg.png | left: FLAIR images, middle: manual delineation of relevant areas, right: manual WMH segmentation.
Image:Stroke lesion mixture model.jpg | Gaussian mixture model for multimodal intensities. yellow: stroke, blue: normal tissue, red: artifacts, green: WMH.
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Binder, Polina Golland, MIT
* Natalia Rost, Jonathan Rosand, MGH

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We have developed some methods for segmentation of white matter hyperintensity (WMH) in FLAIR images of stroke patients. We want to extend our framework to do multi-modal segmentation of multiple tissue types (in our case, stroke lesions, white matter hyperintensity, and normal tissue using T1, FLAIR, DWI, and possibly ADC images). This dataset is particularly challenging due to the low resolution (typically 1mm x 1mm x 7mm) and cropped fields of view in the given images.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Identify intensity and shape signatures of different tissue types across images
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Implemented mixture model for multimodal intensity
* Segmented stroke and white matter hyperintensity, but oversegments: need to find a way to be more specific

</div>
</div>

2014 Project Week:Multi-Tissue Stroke Segmentation

2014-01-10T00:57:12Z

Rameshvs: /* Project Description */

__NOTOC__

<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:WMH_T1.png‎| T1 images in stroke dataset.
Image:WMHseg.png | left: FLAIR images, middle: manual delineation of relevant areas, right: manual WMH segmentation.
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Binder, Polina Golland, MIT
* Natalia Rost, Jonathan Rosand, MGH

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We have developed some methods for segmentation of white matter hyperintensity (WMH) in FLAIR images of stroke patients. We want to extend our framework to do multi-modal segmentation of multiple tissue types (in our case, stroke lesions, white matter hyperintensity, and normal tissue using T1, FLAIR, DWI, and possibly ADC images). This dataset is particularly challenging due to the low resolution (typically 1mm x 1mm x 7mm) and cropped fields of view in the given images.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Identify intensity and shape signatures of different tissue types across images
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
* Implemented mixture model for multimodal intensity
* Segmented stroke and white matter hyperintensity, but oversegments: need to find a way to be more specific

</div>
</div>

File:Stroke lesion mixture model.jpg

2014-01-10T00:55:53Z

Rameshvs: Different tissue classes found using Gaussian mixture models for FLAIR (x) and DWI (y). Yellow corresponds to (oversegmented) stroke lesion, blue corresponds to normal tissue, and red and green are artifacts and white matter hyperintensity respectively.

Different tissue classes found using Gaussian mixture models for FLAIR (x) and DWI (y). Yellow corresponds to (oversegmented) stroke lesion, blue corresponds to normal tissue, and red and green are artifacts and white matter hyperintensity respectively.

2014 Winter Project Week

2014-01-06T19:06:48Z

Rameshvs: /* Stroke */

Back to [[Project Events]], [[AHM_2014]], [[Events]]

__NOTOC__
[[image:PW-SLC2014.png|300px]]

Project Week is a hands on activity -- programming using the open source [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC, NCIGT, and NAC calendars. It is held in the summer at MIT, typically the last week of June, and a shorter version is held in Salt Lake City in the winter, typically the second week of January.

Active preparation begins 6-8 weeks prior to the meeting, when a kick-off teleconference is hosted by the NA-MIC Engineering, Dissemination, and Leadership teams, the primary hosts of this event. Invitations to this call are sent to all NA-MIC members, past attendees of the event, as well as any parties who have expressed an interest in working with NA-MIC. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient NA-MIC coverage for all. Subsequent teleconferences allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams are asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself starts off with a short presentation by each project team, driven using their previously created description, and allows all participants to be acquainted with others who are doing similar work. In the rest of the week, about half the time is spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half is spent in project teams, doing hands-on programming, algorithm design, or clinical application of NA-MIC kit tools. The hands-on activities are done in 10-20 small teams of size 3-5, each with a mix of experts in NA-MIC kit software, algorithms, and clinical. To facilitate this work, a large room is setup with several tables, with internet and power access, and each team gathers on a table with their individual laptops, connects to the internet to download their software and data, and is able to work on their projects. On the last day of the event, a closing presentation session is held in which each project team presents a summary of what they accomplished during the week.

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

Please [[AHM_2014#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

= [[AHM_2014#Agenda|'''AGENDA''']] and Project List=

Please:
* [[AHM_2014#Agenda|'''Click here for the agenda for AHM 2014 and Project Week''']].
* [[#Projects|'''Click here to jump to Project list''']]

=Background and Preparation=

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

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]

=Projects=
* [[2014_Project_Week_Template | Template for project pages]]

==TBI==
*[[2014_Project_Week:TBIatrophy|Multimodal neuroimaging for the quantification of brain atrophy at six months following severe traumatic brain injury]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:TBIdemyelination|Systematic evaluation of axonal demyelination subsequent to traumatic brain injury using structural T1- and T2-weighted magnetic resonance imaging]] (Andrei Irimia, SY Matthew Goh, Carinna M. Torgerson, John D. Van Horn)
*[[2014_Project_Week:BrainAging|Mapping the effect of traumatic brain injury upon white matter connections in the human brain using 3D Slicer]] (Andrei Irimia, John D. Van Horn)
*[[2014_Project_Week:LongitudinalDTI|Patient-specific longitudinal DTI analysis in traumatic brain injury]] (Anuja Sharma, Andrei Irimia, Bo Wang, John D. Van Horn, Martin Styner, Guido gerig)
*[[2014_Project_Week:TBISegmentation|Testing the interactive segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, John D. Van Horn, Guido Gerig)

==Atrial Fibrillation==
*[[2014_Project_Week:MRAFusionRegistration|DEMRI LA Segmentation via Image Fusion (MRA)]] (Josh, Salma, Alan)
*[[2014_Project_Week:LAFibrosisVisualizationModule|LA Fibrosis / Scar Visualization]] (Josh, Salma, Alan)
*[[2014_Project_Week:CARMADocumentation|CARMA Extension Documentation Project]] (Josh, Salma)
*[[2014_Project_Week:GraphCutsLASegmentationModule|LA Segmentation module using multi-column Graph Cuts]] (Gopal, Salma, Josh, Rob, Ross)
*[[2014_Project_Week:AblationSuccessRatePredictionUsingJointImageAndShapeAnalysis|Ablation Success Rate Prediction Using Joint Image And Shape Analysis]](Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum)
*[[2014_Project_Week:GrowCutLevelSetLA|Grow cut, level set integration for interactive LA segmentation]] ( Liangjia Zhu, Ivan Kolesov, Yi Gao, Allen Tannenbaum)

==Huntington's Disease==
*[[2014_Project_Week:DWIDispersion|DWI Dispersion & Compressed Sensing Conversions]] (Hans, CF, Peter Savadjiev, Kent, David)
*[[2014_Project_Week:Modules scripting|Slicer module scripting]] (David)
*[[2014_Project_Week:DWIConverter|DWIConverter]] (Hans, Kent)
*[[2014_Project_Week:Slicer_Based_Surface_Template_Estimation|Slicer Based Surface Template Estimation]] (Saurabh Jain, Steve Pieper, Hans Johnson, Josh Cates)
*[[2014_Project_Week:HD_4DShapes|4D shape analysis: application to HD ]] (James Fishbaugh,Hans Johnson, Guido Gerig)
*[[2014_Project_Week:Shape_Registration_and_Regression|Shape registration and regression in Slicer4 ]] (James Fishbaugh,Hans Johnson, Guido Gerig)

==Head and Neck Cancer==
*[[2014_Project_Week:DIR_validation|DIR Validation]] (Nadya and Greg)
*[[2014_Project_Week:Hybrid_bspline|Hybrid B Spline]] (Nadya, Greg, Steve)
*[[2014_Project_Week:CarreraSlice|Interactive Segmentation]] (Ivan, LiangJia, Nadya, Yi, Greg, Allen)

==Slicer4 Extensions==
*[[2014_Project_Week:ShapePopulationViewer|Surface Visualization - ShapePopulationViewer]] (Alexis Girault, Francois Budin, Beatriz Panaigua, Martin Styner)
*[[2014_Project_Week:DTIAnalysisPipeline|DTI Analysis Pipeline as Slicer4 Extensions]] (Francois Budin, Martin Styner)

==Cardiac==
*[[2014_Project_Week:CardiacStemCellMonitoring|Monitoring engrafted stem cells in cardiac tissue with time series manganese enhanced MRI]] (Karl Diedrich)
*[[2014_Project_Week:CardiacCongenitalSegmentation|Whole-heart segmentation of cardiac MR images in congenital heart defect cases]] (Danielle Pace, Polina Golland)

==Stroke==

*[[2014_Project_Week:Multi-Tissue_Stroke_Segmentation|Multi-Tissue Stroke Segmentation]] (Polina Binder, Ramesh Sridharan, Polina Golland)
*[[2014_Project_Week:Processing_Pipelines|Processing Pipelines]] (Ramesh Sridharan, Polina Golland)

==Brain Segmentation==
*[[2014_Project_Week:MultiAtlas_MultiImage_Segmentation|Multi-Atlas based Multi-Image Segmentation]] (Minjeong Kim, Xiaofeng Liu, Jim Miller, Dinggang Shen)

==Image-Guided Interventions==
*[[2014_Project_Week:OpenIGTLink| OpenIGTLink Interface: New data types and structures]] (Junichi Tokuda, Andras Lasso, Steve Piper, ???)
*[[2014_Project_Week:Ultrasound Visualization and Navigation in Neurosurgery|Ultrasound Visualization and Navigation in Neurosurgery]] (Matthew Toews, Alireza Mehrtash, Csaba Pinter, Andras Lasso, Steve Pieper, William M. Wells III)
*[[2014_Project_Week:PercutaneousApproachAnalysis| Percutaneous Approach Analysis]] (Atsushi Yamada, Junichi Tokuda, Koichiro Murakami, ??)
*[[2014_Project_Week:EndoscopeConsole| Endoscope Console]] (Atsushi Yamada, Junichi Tokuda, ??)
*[[2014_Project_Week:Statistical Shape Model for robotic spine surgery| Statistical Shape Model for robotic spine surgery]] (Marine Clogenson, ???)
*[[2014_Project_Week:ImmersiveVR| Immersive VR devices]] (Franklin King, Andras Lasso)

==Radiation Therapy==
*[[2014_Project_Week:DICOM_RT|DICOM RT Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:DICOM_SRO|DICOM Spatial Registration Export]] (Greg Sharp, Kevin Wang, others??)
*[[2014_Project_Week:Registration_Evaluation|Interactive Registration and Evaluation]] (Kevin Wang, Steve Pieper, Greg Sharp)
*[[2014_Project_Week:External_Beam_Planning|External Beam Planning Visualization]] (Kevin Wang, Greg Sharp, Csaba Pinter)

==TMJ-OA==
* [[2014_Winter_Project_Week:Constrain Fiducial along Suface|Constrain Fiducial along Suface]] (Vinicius Boen, Nicole Aucoin, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Cropping Multiple Surfaces|Cropping multiple surfaces simultaneously]] (Alexander, Jc, Steve, Vinicius, Beatriz Paniagua)
* [[2014_Winter_Project_Week:Color Code Tables|Color Coded Tables]] (Vinicius Boen, Beatriz Paniagua, Nicole Aucoin, Steve Pieper, Francois Budin)
* [[2014_Winter_Project_Week:4DShape Analysis of mandibular changes|4DShape Analysis of mandibular changes]] (Vinicius Boen, James Fishbaugh, Guido Gerig)

==Chronic Obstructive Pulmonary Disease ==
* [[2014_Winter_Project_Week:CIP Core|Chest Imaging Platform (CIP) - Core Infrastructure]] (Raul San Jose, Rola Harmouche, Pietro Nardelli, James Ross)
* [[2014_Winter_Project_Week:CIP Infrastructure Testing and SuperBuild|CIP Testing and SuperBuild]] (James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Slicer MRML| Slicer CIP- MRML consolidation]] (Pietro Nardelli, Rola Harmouche, James Ross, Raul San Jose)
* [[2014_Winter_Project_Week:Slicer CIP Modules| Slicer CIP- Modules]] (Rola Harmouche, Pietro Nardelli, James Ross, Raul San Jose)

==[http://qiicr.org QIICR]==

*[[2014_Project_Week:4D_NIfTI_Multivolume|4D NIfTI Multivolume Support]] (Jayashree, Andrey, Jim, John)
*[[2014_Project_Week:RT_FormatConversions|RT and ITK Format Conversions]] (Jayashree, Andras, Csaba. John)
*[[2014_Project_Week:BatchConvertDICOM|Python Scripting Slicer DICOM read/write to convert segmentation objects]] (Jayashree, Andrey, Alireza, Steve, Jc, Hans, John)
*[[2014_Project_Week:PkModeling_user_tool|User module for DCE modeling]] (Andrey, Jayashree, Jim, Alireza, Steve, Ron)
*[[2014_Project_Week:DICOM_enhanced_multiframe|DICOM enhanced multiframe object support]] (Andrey, Alireza, David Clunie, Jayashree, Steve, Reinhard, Jim)
*[[2014_Project_Week:Quantitative_Index_Computation|Quantitative Index Computation]] (Ethan Ulrich, Reinhard Beichel, Nicole, Andrey, Jim)
*[[2014_Project_Week:TCIA Browser Extension in Slicer|TCIA Browser Extension in Slicer]] (Alireza, Andrey, Steve, Ron)
*[[2014_Project_Week:Slicer DICOM Module Interface Redesign|Slicer DICOM Module Interface Redesign]] (Alireza, Andrey, Steve, Ron)

==Infrastructure==
*[[2014_Project_Week:MRMLSceneSpeedUp|MRML Scene speed up]] (Jc, Andras Lasso)
*[[2014_Project_Week:MultidimensionalDataSupport|Multidimensional data support]] (Andras Lasso, Andriy Fedorov, Steve Pieper, JC, Kevin Wang)
*[[2014_Project_Week:MarkupsModule|Markups Module]] (Nicole Aucoin)
* [[2014_Winter_Project_Week:Logging|Logging (standardization, logging to file)]] (Nicole Aucoin, Steve Pieper, Jc, Andras Lasso, Csaba Pinter, ???)
*[[2014_Project_Week:CLI|CLI]] (Jim Miller)
* [[2014_Winter_Project_Week:Steered Registration|Steered Registration (LandmarkRegistration module)]] (Steve, Greg, Kevin, Vinicius, Marcel)
* [[2014_Winter_Project_Week:MRB Extension Dependencies|MRB Extension Dependencies]] (Steve, Jc, Jim, Nicole, Alex)
* [[2014_Winter_Project_Week:SubjectHierarchy|Subject hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper, Jc, Jayashree, John, Alireza, Andrey)
* [[2014_Winter_Project_Week:IntegrationOfContourObject|Integration of Contour object]] (Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:NonlinearTransforms|Integration nonlinear transforms]] (Alex Yarmarkovich, Csaba Pinter, Andras Lasso, Steve Pieper, ???)
* [[2014_Winter_Project_Week:ParameterSerialization | JSON Parameter Serialization]] (Matt McCormick, Steve Pieper, Jim Miller)
* [[2014_Winter_Project_Week:XNATSlicerLink| 3DSlicer annotations in XNAT]] (Erwin Vast, Nicole Aucoin, Andrey Fedorov)
* [[2014_Winter_Project_Week:PlanarImage|Planar Images]] (Franklin King, Csaba Pinter, Andras Lasso)

2014 Project Week:Processing Pipelines

2014-01-06T19:05:01Z

Rameshvs: Created page with '__NOTOC__ <gallery> Image:PW-SLC2014.png|Projects List Image:Pipeline_visualization_sketch.png‎| Visualization of pipeline from code </ga…'

File:Pipeline visualization sketch.png

2014-01-06T19:03:42Z

Rameshvs: Preliminary version of visualized processing pipeline

Preliminary version of visualized processing pipeline

2014 Winter Project Week

2013-12-17T19:18:31Z

Rameshvs: /* Stroke */

2014 Project Week:Multi-Tissue Stroke Segmentation

2013-12-17T19:16:33Z

Rameshvs: Created page with '__NOTOC__ <gallery> Image:PW-SLC2014.png|Projects List Image:WMH_T1.png‎| T1 images in stroke dataset. Image:WMHseg.png | left: FLAIR ima…'

__NOTOC__

<gallery>
Image:PW-SLC2014.png|[[2014_Winter_Project_Week#Projects|Projects List]]
Image:WMH_T1.png‎| T1 images in stroke dataset.
Image:WMHseg.png | left: FLAIR images, middle: manual delineation of relevant areas, right: manual WMH segmentation.
</gallery>

==Key Investigators==

* Ramesh Sridharan, Adrian Dalca, Polina Binder, Polina Golland, MIT
* Natalia Rost, Jonathan Rosand, MGH

==Project Description==

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
We have developed some methods for segmentation of white matter hyperintensity (WMH) in FLAIR images of stroke patients. We want to extend our framework to do multi-modal segmentation of multiple tissue types (in our case, stroke lesions, white matter hyperintensity, and normal tissue using T1, FLAIR, DWI, and possibly ADC images). This dataset is particularly challenging due to the low resolution (typically 1mm x 1mm x 7mm) and cropped fields of view in the given images.
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
* Identify intensity and shape signatures of different tissue types across images
</div>
<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Progress</h3>
</div>
</div>

Projects:fMRIClustering

2012-11-28T20:06:46Z

Rameshvs:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Improving fMRI Analysis using Supervised and Unsupervised Learning =

One of the major goals in the analysis of fMRI data is the detection of regions of the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements.

= Clustering for Discovering Structure in the Space of Functional Selectivity =

We are devising clustering algorithms for discovering structure in the functional organization of the high-level visual cortex. It is suggested that there are regions in the visual cortex with high selectivity to certain categories of visual stimuli; we refer to these regions as /functional units/. Currently, the conventional method for detection of these regions is based on statistical tests comparing response of each voxel in the brain to different visual categories to see if it shows considerably higher activation to one category. For example, the well-known FFA (Fusiform Face Area) is the set of voxels which show high activation to face images. We use a model-based clustering approach to the analysis of this type of data as a means to make this analysis automatic and further discover new structures in the high-level visual cortex.

We formulate a model-based clustering algorithm that simultaneously
finds a set of activation profiles and their spatial maps from fMRI time courses. We validate
our method on data from studies of category selectivity in the visual
cortex, demonstrating good agreement with findings from prior
hypothesis-driven methods. This hierarchical model enables functional group analysis
independent of spatial correspondence among subjects. We have also developed a co-clustering extension of this
algorithm which can simultaneously find a set of clusters of voxels and categories
of stimuli in experiments with diverse sets of stimulus categories. Our model is nonparametric, learning the numbers of clusters in both domains as well as the cluster parameters.

Fig. 1 shows the categories learned by our algorithm on a study with 8 subjects. We split trials of each image into two groups of equal size and consider
each group as an independent stimulus forming a total of 138
stimuli. Hence, we can examine the consistency of our stimulus categorization with respect to identical trials. Stimulus pairs
corresponding to the same image are generally assigned to the same
category, confirming the consistency of the resuls across
trials. Category 1 corresponds to the scene images and, interestingly, also includes all images of
trees. This may suggest a high level category structure that is not
merely driven by low level features. Such a structure is even more
evident in the 4th category where images of a tiger that has a large
face join human faces. Some other animals are clustered together with human bodies in categories 2 and
9. Shoes and cars, which have similar shapes, are clustered together
in category 3 while tools are mainly found in category 6.

{|
|+ '''Fig 1. Categories learned from 8 subjects'''
|align="center"|[[Image:Category_singlefile1.png |thumb|800px]]
|}

Fig. 2 shows the cluster centers, or activation profiles, for the first 13 of 25 clusters learned by our method. We see salient category structure in our profiles. For instance, system 1 shows lower responses to cars, shoes, and tools compared to other stimuli. Since the images representing these three categories in our experiment are generally smaller in terms of pixel size, this system appears selective to lower level features (note that the highest probability of activation among shoes corresponds to the largest shoe 3). System 3 and system 8 seem less responsive to faces compared to all other stimuli.

{|
|+ '''Fig 2. System profiles of posterior probabilities of activation for each system to different stimuli. The bar heights correspond to the posterior probability of activation.'''
|align="center"|[[Image:Hdpprofs_all_1.png |thumb|800px]]
|}

Fig. 3 shows the membership maps for the systems 2, 9, and 12, selective for bodies, faces, and scenes, respectively, which our model learns in a completely unsupervised fashion from the data. For comparison, Fig. 4 shows the significance maps found by applying the conventional confirmatory t-test to the data from the same subject. While significance maps appear to be generally larger than the extent of systems identified by our method, a close inspection reveals that system membership maps include the peak voxels for their corresponding contrasts.

{|
|+ '''Fig 3. Membership probability maps corresponding to systems 22, 9, and 12, selective respectively for bodies (magenta), scenes (yellow), and faces (cyan) in one subject.'''
|align="center"|[[Image:Sys_2_9_12_subj1.png |thumb|800px]]
|}

{|
|+ '''Fig 4. Map representing significance values for three contrasts: bodies-objects (magenta), faces-objects (cyan), and scenes-objects (yellow) in the same subject. Lighter colors correspond to higher significance.'''
|align="center"|[[Image:Sys_2_9_12_subj1.png |thumb|800px]]
|}

'''''Earlier work'''''

Fig. 5 compares the map of voxels assigned to a face-selective profile by an earlier version of our algorithm with the t-test's map of voxels with statistically significant (p<0.0001) response to faces when compared with object stimuli. Note that in contrast with the hypothesis testing method, we don't specify the existence of a face-selective region in our algorithm and the algorithm automatically discovers such a profile of activation in the data.

{|
|+ '''Fig 5. Spatial maps of the face selective regions found by the statistical test (red) and our mixture model (dark blue). Maps are presented in alternating rows for comparison. Visually responsive mask of voxels used in our experiment is illustrated in yellow and light blue.'''
|align="center"|[[Image:mit_fmri_clustering_mapffacompare.PNG |thumb|800px]]
|}

'''''Hierarchical Model for Exploratory fMRI Analysis without Spatial Normalization'''''

Building on the work on the clustering model for the domain specificity, we develop a hierarchical exploratory method for simultaneous parcellation of multisub ect fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to ﬁt the model to the data. The resulting algorithm ﬁnds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on the same visual fMRI study as before. Fig. 6 shows the scene-selective parcel in 2 different subjects. Parcel-level spatial correspondence is evident in the figure between the subjects.

<table>
<tr> <th> '''Fig 6. The map of the scene selective parcels in two different subjects. The rough location of the scene-selective areas PPA and TOS, identified by the expert, are shown on the maps by yellow and green circles, respectively.'''
<tr>
<td align="center">
[[Image:mit_fmriclustering_hierarchicalppamapsubject1.jpg |650px]]
<td align="center">
[[Image:mit_fmriclustering_hierarchicalppamapsubject2.jpg |650px]]
</table>

= Key Investigators =

* MIT: Danial Lashkari, Archana Venkataraman, Ramesh Sridharan, Ed Vul, Nancy Kanwisher, Polina Golland.
* Harvard: J. Oh, Marek Kubicki, Carl-Fredrik Westin.

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3AfMRIClustering&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on fMRI clustering]

Project Week Results: [[2008_Summer_Project_Week:fMRIconnectivity|June 2008]]

[[Category:fMRI]]

Projects:QuantitativeSusceptibilityMapping

2012-11-28T19:47:45Z

Rameshvs: Undo revision 78400 by Rameshvs (Talk)

= Introduction =

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease [1]. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal (in a gradient echo sequence, the observed field is proportional to the MR phase).

= Description =

In MRI, magnetic susceptibility differences cause measurable perturbations in the local magnetic field that can be modeled as the convolution of a dipole-like kernel with the spatial susceptibility distribution. In the Fourier domain, the kernel exhibits zeros at the magic angle, preventing direct inversion of the field map; also, limited observations make the problem ill-posed. The observed data is also corrupted by confounding fields (ie. those from tissue/air interfaces, mis-set shims, and other non-local sources). Previous work has shown that MR images can be successfully reconstructed from under-sampled observations by exploiting the sparsity of in-vivo data under various transforms using methods from compressed sensing [2]. In susceptibility estimation, the forward model results in under-sampling of the data in the Fourier domain, but accurate estimates can be obtained using the Laplacian and L1 norm, which promote sparse solutions while removing external field artifacts. Our variational method for susceptibility estimation is described in Figs. 1-2.

{|
|[[File:Namic wiki fig1.png|thumb|400px|Fig 1. Relevant notation]]
|}

{|
|[[File:Latex pdf zoomed to paint equations.PNG|thumb|400px|Fig 2. Applying the Laplacian to the forward model in [1] eliminates non-local phase artifacts to give [2]. The first term in [3] provides regularization, penalizing large differences in spatial frequency relative to Magnitude data, while the second penalizes departures from [2], enforcing agreement of high frequency phase effects.]]
|}

== Forward Model (Eq. 1) ==

The forward model relates the perturbing field to the unknown susceptibility through a local term and convolution of the second z-derivative of the Green’s function of the Laplacian with the unknown susceptibility map [3].

== Bias Field Elimination (Eq. 2) ==

Applying the Laplacian removes non-local phase effects such as shim fields, which are a solution to the Laplace equation.

== Objective Function (Eq. 3) ==

The first term provides regularization, penalizing solutions with large differences in spatial frequency structure relative to the magnitude image.
The second term penalizes departures from Eq. 2, by enforcing agreement of high frequency phase effects while eliminating low order bias fields.

== Data Acquisition ==

Cylindrical and rectangular phantoms were made using Magnevist (gadopentetate dimeglumine) solutions of 0.5, 1.0, 2.0, and 3.0 mM corresponding to susceptibility values of 0.15, 0.31, 0.62, and 0.94 ppm [4,5]. Field maps were obtained using a 3D multi-echo GRE sequence on a 3T Siemens Trio MRI.

= Results =

Application of the Laplacian removes substantial inhomogeniety in the field map in both phantoms as shown in Fig. 3 (Rectangular phantom) and Fig. 4 (Cylindrical phantom). Rectangular phantom: mean estimated susceptibility values for water and Magnevist were -9.049 and 0.6273 ppm, with true values of -9.050 and 0.6270 ppm. Cylindrical phantom: the estimated susceptibility map allowed different concentrations of Magnevist to be clearly identified and reasonable estimates were obtained in the presence of significant noise and bias due to external field effects.

{|
|[[File:Box mag.jpg|thumb|300|Fig 3a. Magnitude Image]]
|[[File:Box fmap.png|thumb|300|Fig 3b. Field map]]
|[[File:Box fmap lp.png|thumb|300|Fig 3c. Laplacian of the Field]]
|[[File:Box susc.png|thumb|300|Fig 3d. Estimated Susceptibility (ppm)]]
|}

{|
|[[File:Cyl mag.png|thumb|300|Fig 4a. Magnitude Image]]
|[[File:Cyl fmap.png|thumb|300|Fig 4b. Field map]]
|[[File:Cyl fmap lp.png|thumb|300|Fig 4c. Laplacian of the Field]]
|[[File:Cyl susc.png|thumb|300|Fig 4d. Estimated Susceptibility (ppm)]]
|[[File:Susc plot2.png|thumb|100|Fig 4e. Estimated vs. True mean susceptibility values for each tube (ppm)]]
|}

= Future Directions =

Future work will focus on quantifying magnetic susceptibility and iron content in the brain. Further development of the method described above has generated the preliminary results shown below in Fig 5.

{|
|[[File:Miccai fig1 crop.png|thumb|600px|Fig 5. The field map (left), laplacian of the field (center) and estimated susceptibility map (right) for a young healthy subject is shown. Taking the Laplacian of the fieldmap successfully eliminates the substantial biasfields in the observed field. The estimated susceptibility map shares similar high frequency structure with the Laplacian of the observed field while low frequency structure is preserved by additional modeling constraints.]]
|}

= References =

1. Zecca L, et al. Nat Rev Neurosci, 5:863{73, Nov 2004.

2. Lustig M,et al. MRM. 2007. 58(6):1182.

3. Jenkinson M, et al. MRM. 2004. 52(3):471.

4. de Rochefort L, et al. MRM.2010. 63(1):194.

5. Weisskoff RM, et al. MRM. 1992. 24(2):375.

= Key Investigators =

* MIT: Clare Poynton, Elfar Adalsteinsson
* Harvard/BWH: William Wells
* Stanford: Adolf Pfefferbaum, Edith Sullivan

Projects:QuantitativeSusceptibilityMapping

2012-11-28T19:42:15Z

Rameshvs:

2012 Summer Project Week

2012-06-22T05:54:11Z

Rameshvs:

Back to [[Events]]

[[image:PW-MIT2012.png|200px]]

*'''Dates:''' June 18-22, 2012
*'''Location:''' MIT

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 18
!style="width:18%" |Tuesday, June 19
!style="width:18%" |Wednesday, June 20
!style="width:18%" |Thursday, June 21
!style="width:18%" |Friday, June 22
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''9am-10am:''' [[2012 Project Week Breakout Session: Slicer4|What's new in Slicer4 (Charts - Jim, DICOM - Steve, Multivolume - Andrey)]] <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
<br>----------------------------------------<br>
'''10-11am''' [[2012 Project Week Breakout Session:Slicer4 Python Q&A|Slicer4 Python Q&A]] <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9am-11pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans)
[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
<br>----------------------------------------<br>
'''10am-12pm: <font color="#4020ff">Computation Core PIs: closed meeting with Ron:'''</font><br>
[[MIT_Project_Week_Rooms#Star|Star Room]]
|'''9am-12pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Summer Project Week Breakout Session:Slicer in Networked Environment|Slicer in Networked Environment]] (Junichi)
[[MIT_Project_Week_Rooms#Kiva_32-G449|Grier Room]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' [[2012 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (JC) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''3-4pm:''' [[2012_Tutorial_Contest|Tutorial Contest Presentations]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''4-5pm:''' [[2012 Summer Project Week Breakout Session:Slicer DICOM|Breakout Session: DICOM, Networking, RT, Segmentations]] (Steve, Greg, Andras, Andre)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Star Room]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2012|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3-30pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Summer Project Week Breakout Session:QtTesting|QtTesting]] (JC)
|'''1-3pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Summer Project Week Breakout Session:Ultrasound|Ultrasound]] (Tamas)
[[MIT_Project_Week_Rooms#Kiva_32-G449|Grier Room]]
<br>----------------------------------------<br>
'''3:00-4:00pm''' [[2012_Summer_Project_Week:LeanSlicer|Lean Slicer (Andras)]]<br>
[[MIT_Project_Week_Rooms#Kiva_32-G449|Grier Room]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

==Projects==

Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template THIS TEMPLATE] to create project pages for this event.

==='''Neurosurgery, Brain and Spine, Traumatic Brain Injury'''===

# [[Semiautomatic longitudinal segmentation of MR volumes in traumatic brain injury]] (Andrei Irimia, Micah Chambers, Bo Wang, Marcel Prastawa, Danielle Pace, Stephen Aylward, Jack van Horn, Guido Gerig)
# [[2012_Summer_Project_Week:4D_Segmentation_TBI|4D Segmentation of longitudinal MRI of TBI patients]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig)
# [[2012_Summer_Project_Week:Intraoperative_Tract_Detection | Intraoperative White Matter Tract Detection Module]] (Lauren O'Donnell, Isaiah Norton)
# [[2012_Summer_Project_Week:Ultrasound_Aberration_Correction | An Ultrasound-based Method for Aberration Correction in TCFUS]] (Jason White, Greg Clement)
# [[2012_Summer_Project_Week:Early_Dementia_Diagnostic |Early Dementia Diagnostic Tools]] (Marcel Koek, Sonia Pujol)
# [[2012_Summer_Project_Week:Radnostics |Spine Segmentation & Osteoporosis Detection In CT Imaging Studies]] (Anthony Blumfield, Ron Kikinis)

==='''Radiation Therapy'''===
#[[2012_Summer_Project_Week:Atlas_based_segmentation_for_head_and_neck| Atlas-based segmentation for head and neck]] (Amelia Arbisser, Nadya Shusharina, James Shackleford, Greg Sharp, Polina Golland)
#[[2012_Summer_Project_Week:Overlapping_structures| First class structure set support in Slicer]] (Greg Sharp, James Shackleford, Steve Pieper)
#[[2012_Summer_Project_Week:PlastimatchIntegration| Plastimatch loadable module]] (James Shackleford, Greg Sharp)
#[[2012_Summer_Project_Week:Deformable_Registration_for_Head_and_Neck| Deformable Registration for Head and Neck ]] (Ivan Kolesov, Greg Sharp, Yi Gao, Allen Tannenbaum)
#[[2012_Summer_Project_Week:SlicerRT| Radiotherapy extensions for Slicer 4]] (Andras Lasso, Csaba Pinter, Kevin Wang)
#[[2012_Summer_Project_Week:PET_Image_Analysis | SUV Threshold Computation]] (Nadya Shusharina, Greg Sharp)

==='''Huntington's Disease'''===
# [[2012_Summer_Project_Week:Nipype Integration|Slicer/Nipype Integration]] (Hans Johnson)
# [[2012_Summer_Project_Week:DicomToNrrd|DicomToNrrdConverter Integration]] (Kent Williams, Hans Johnson)
# [[2012_Summer_Project_Week:4D shape analysis|4D Shape Analysis: Software Tools]] (James Fishbaugh, Marcel Prastawa, Guido Gerig)
# [[2012_Summer_Project_Week:DTI-Reg|DTI atlas based fiber analysis]] (Francois Budin)

==='''Atrial Fibrillation'''===
# [[2012_Summer_Project_Week:UtahCardiacRegistration|Cardiac MRI Registration Module]] (Greg Gardner, Alan Morris, Danny Perry, Josh Cates, Rob MacLeod)
# [[2012_Summer_Project_Week:UtahAutoScar|Automatic Left Atrial Scar Detection]] (Greg Gardner, Danny Perry, Alan Morris, Josh Cates, Rob MacLeod)
# [[2012_Summer_Project_Week:UtahInhomogeneity|MRI Inhomogeneity Correction Filter]] (Greg Gardner, Alan Morris, Eugene Kholmovski, Josh Cates, Danny Perry, Rob MacLeod)
# [[2012_Summer_Project_Week:VecReg|Vector-Valued Cardiac MRI Registration]] (Yi Gao, Josh Cates, Liang-Jia Zhu, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
# [[2012_Summer_Project_Week:RidgeExtractionAtrialWallSegmentation|Perceptual Ridge Extraction for Atrial Wall Segmentation in MRI]] (Arie Nakhmani, Allen Tannenbaum)

==='''Device Integration with Slicer and Image Guided Therapy'''===
# [[2012_Summer_Project_Week:SlicerWeb|Web Interface to Slicer 4]] (Steve Pieper)
# [[2012_Summer_Project_Week:OpenIGTLinkIF|Improvement of OpenIGTLink IF for Slicer 4]] (Junichi Tokuda, Laurent Chauvin)
# [[2012_Summer_Project_Week:LeanSlicer|Lean Slicer to facilitate regulatory approval]] (Andras Lasso, Chris Wedlake)
# [[2012_Summer_Project_Week:LiveUltrasound|Live Ultrasound]] (Tamas Ungi, Andinet Enquobahrie, Junichi Tokuda)
# [[2012_Summer_Project_Week:BK-PLUS_Integration|Integration of BK ProFocus US with Slicer via PLUS library]] (Andras Lasso, Andrey Fedorov, Isaiah Norton, Saman)
# [[2012_Summer_Project_Week:TransformRecorder|Transform Recorder and other IGT modules]] (Simrin Nagpal, Tamas Ungi)
# [[2012_Summer_Project_Week:Open_source_electromagnetic_trackers_using OpenIGTLink|Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)
# [[2012_Summer_Project_Week:iGyne|iGyne for Gynecological Cancer Brachytherapy]] (Xiaojun Chen, Jan Egger, Tina Kapur, Steve Pieper)
#[[2012_Summer_Project_Week:Interactive_Needle_Segmentation|Interactive Needle Segmentation for Gynecological Cancer Brachytherapy]] (Nabgha Farhat, Neha Agrawal, Jan Egger, Tina Kapur, Steve Pieper)
# [[2012_Summer_Project_Week:VertebraCTUSReg|Single Vertebra CT-US Registration]] (Saman Nouranian, Samira Sojoudi, Simrin Nagpal, Tamas Ungi, David Welch)
# [[2012_Summer_Project_Week:Fast Fiducial Registration|Fast Fiducial Registration Module]] (David Welch, Hans Johnson, Nicole Aucoin, Ron Kikinis)
# [[2012_Summer_Project_Week:SteeredRegistration|Steered Registration for Image Guided Therapy]] (Guillaume Pernelle BWH, Jan Egger, Tina Kapur, Steve Pieper, Jim Miller, Kunlin Cao)
# [[2012_Summer_Project_Week:4D_Ultrasound_Slicer4|4D Ultrasound on Slicer4]] (Laurent Chauvin, Nobuhiko Hata)
# [[2012_Summer_Project_Week:Kinect4Slicer|Kinect4Slicer]] (Laurent Chauvin, Nobuhiko Hata)
# [[2012_Summer_Project_Week:Needle Tip Tracking |Needle Tip Tracking for complex MR images]] (Atsushi Yamada, Nobuhiko Hata)

==='''General Segmentation'''===
#[[2012_Summer_Project_Week:SemiAutomatedAirwaySegmentationfrom0.64mmLungCTDatasets|Semi-automated airway segmentation from 0.64mm lung CT datasets]] (Pietro Nardelli, Padraig Cantillon-Murphy, Raul San Jose Estepar)
#[[Loading and segmentation of histopathology imaging for radiological-pathological correlation]] (Tobias Penzkofer, Andrey Fedorov)
#[[2012_Summer_Project_Week:ABC_Slicer4|Porting ABC extension to Slicer 4]] (Marcel Prastawa, Bo Wang, Guido Gerig)
# [[2012_Summer_Project_Week:QuantitativePETImageAnalysisModule|Quantitative PET Image Analysis Module]] (Markus Van Tol)

==='''General Registration'''===
# [[2012_Summer_Project_Week:NiftyReg|NiftyReg integration]] (Marc Modat, Sonia Pujol)
#[[2012_Summer_Project_Week:ElastixIntegration|Elastix integration]] (Stefan Klein, Sonia Pujol)
# [[2012_Summer_Project_Week:DTIRegistration| Highly Deformable DTI Registration for cases with large pathological variations]] (Aditya Gupta, Martin Styner, Matthew Toews)
# [[2012_Summer_Project_Week:DifficultRegistration| Registration of Difficult Images]] (Matthew Toews, Stefan Klein, Marc Modat, Aditya Gupta, Martin Styner, Petter Risholm, Dominik Meier, William Wells)

==='''Informatics'''===
# [[2012_Summer_Project_Week:AIM_for_QIN|Applicability of AIM to QIN use cases]] (Andrey Fedorov, Reinhard Beichel, Jayashree Kalpathy-Cramer, Pat Mongkolwat, Daniel Rubin)
# [[2012_Summer_Project_Week:Reporting|Reporting module]] (Andrey, Nicole, Steve, Ron, Pat)
# [[2012_Summer_Project_Week:CMake_for_AIM_API|CMake-fying AIM API]] (Pat Mongklowat, Vlad Kleper, Andrey Fedorov)

==='''Infrastructure'''===

# [[2012_Summer_Project_Week:SelfTesting|Built-In Self-Testing (BIST) for Slicer]] (Steve, Julien, Jc, Sonia)
# [[2012_Summer_Project_Week:AnnotationModule|Annotation module redesign for Slicer]] (Nicole)
# [[2012_Summer_Project_Week:MultiVolumeSupport|Multivolume support]] (Andrey, Jim, Brendan Moloney)
# [[2012_Summer_Project_Week:PythonCLIandWidget|Python CLI modules (Demian, JC, Julien, Steve)]].
# [[2012_Summer_Project_Week:Charting|Charting]] (Jim)
# [[2012_Summer_Project_Week:GPUEditor|GPU Editor Effects]] (Steve, Jim)
# [[2012_Summer_Project_Week:XTK|XTK/WebGL Exporter]] (Daniel, Nicolas - Boston Children's Hospital)
# [[2012_Summer_Project_Week:EventOptimization|Callback/Events/Observation best practice + Performance bottleneck discussion (Julien, Steve,...)]]
# [[2012_Summer_Project_Week:XNATSlicerIntegration|XNAT/Slicer Integration]] (Sunil, Dan, Steve,...)
# [[2012_Summer_Project_Week:ITKv4 Integration|ITKv4 Integration]] (Hans Johnson, Julien Finet, Jim). See [http://www.na-mic.org/Bug/view.php?id=2007 #2007]
# [[2012_Summer_Project_Week:SimpleITK Integration|SimpleITK Integration]] (Hans Johnson, Bradley Lowekamp)
# [[2012_Summer_Project_Week:Threat Modeling|Threat Modeling]] (JC, J2, Anthony, Steve)
# [[2012_Summer_Project_Week:CUDA Volume Rendering Extension|CUDA Volume Rendering as Extension]] (J2, Elvis)

==Background==

We are pleased to announce the 15th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 26th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [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], [http://www.cimit.org CIMIT], and OCAIRO. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

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

== Logistics ==
*'''Dates:''' June 18-22, 2012.
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please click [https://www.regonline.com/namic2012summerprojweek HERE] to do an on-line registration for the meeting that will allow you to pay by credit card. No checks will be accepted.
*'''Registration Fee:''' $300 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' No room blocks have been reserved in any area hotel. Please select a [http://web.mit.edu/institute-events/visitor/stay.html hotel of your choice] and make reservations as early as possible. Some area hotels are:
**marriott cambridge center
**marriott residence inn kendall square
**le meridien central square
**hotel marlowe cambridge
**royal sonesta hotel cambridge

== Preparation ==
# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 26, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 10, all participants to add a one line title of their project to #Projects
# By 3pm ET on Thursday June 7, all project leads to complete [[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 3pm on June 14: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)
## 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. XNAT/MIDAS). 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.)
## Where possible, 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. (Matt)
# 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.
## Projects to develop extension modules should be built against the latest Slicer4 trunk.

==Registrants==

Do not add your name to this list- it is maintained by the organizers based on your paid registration. ([http://www.regonline.com/Register/Checkin.aspx?EventID=1089602 Please click here to register.])

#Anderson, Peter, retired, traneus@verizon.net
#Arbisser, Amelia, MIT, arbisser@mit.edu
#Aucoin, Nicole, BWH, Nicole@bwh.harvard.edu
#Aylward, Stephen, Kitware, stephen.aylward@kitware.com
#Blevins, Scott, BWH, stblevins@gmail.com
#Blumfield, Anthony, Radnostics, Anthony.Blumfield@Radnostics.com
#Budin, Francis, NIRAL-UNC, fbudin@unc.edu
#Cao, Kunlin, GE Research, cao@ge.com
#Chambers, Micah, UCLA, micahcc@ucla.edu
#Chauvin, Laurent, SPL, lchauvin@bwh.harvard.edu
#Chen, Elvis, Robarts, chene@robarts.ca
#Chen, Xiaojun, SPL, xiaojun@bwh.harvard.edu
#Datar, Manasi, Utah SCI, datar@sci.utah.edu
#Diedrich, Karl, AZE R&D, karl.diedrich@azeresearch.com
#Egger, Jan, BWH, egger@bwh.harvard.edu
#Farhat, Nabgha, SPL, nfarhat@bwh.harvard.edu
#Fedorov, Andriy, BWH, fedorov@bwh.harvard.edu
#Fillion-Robin, Jean-Christophe, Kitware, jchris.fillionr@kitware.com
#Finet, Julien, Kitware, julien.finet@kitware.com
#Fishbaugh, James, SCI, jfishbau@sci.utah.edu
#Gao, Yi, BWH, gaoyi@bwh.harvard.edu
#Gardner, Greg, SCI, ggardner@sci.utah.edu
#Golland, Polina, MIT CSAIL, polina@csail.mit.edu
#Gouaillard, Alexandre, A*STAR, agouaillard@gmail.com
#Gupta, Aditya, NIRAL UNC, aditya_gupta@med.unc.edu
#Irimia, Andrei, UCLA, andrei.irimia@loni.ucla.edu
#Jagadeesan, Jayender, SPL, jayender@bwh.harvard.edu
#Johnson, Hans, Univ Iowa, hans-johnson@uiowa.edu
#Kalpathy-Cramer, Jayashree, MGH, kalpathy@nmr.mgh.harvard.edu
#Kapur, Tina, BWH HMS, tkapur@bwh.harvard.edu
#Kikinis, Ron, HMS, kikinis@bwh.harvard.edu
#Klein, Stefan, (cancelled registration) Erasmus MC, s.klein@erasmusmc.nl
#Kleper, Vladimir, Northwestern Univ, vkleper@northwestern.edu
#Koek, Marcel, Erasmus MC, m.koek@erasmusmc.nl
#Kolesov, Ivan, GA Tech, ivan.kolesov@gatech.edu
#Kumar, Sunil, Washington Univ St Louis, kumars@mir.wustl.edu
#Lasso, Andras, Queen's Univ, lasso@cs.queensu.ca
#Lowekamp, Bradley, NLM/NIH, bradley.lowekamp@nih.gov
#Macule, Raul, AZE R&D, raul.macule@azeresearch.com
#Mastrogiacomo, Katie, SPL, BWH, kmast@bwh.harvard.edu
#Meier, Dominik, BWH, meier@bwh.harvard.edu
#Mercea, Paul, SPL, pmercea@bwh.harvard.edu
#Miller, Jim, GE Research, millerjv@ge.com
#Mizutani, Tatsushi, Nagoya Univ, tatsushi0207@me.com
#Modat, Marc, Univ College London, m.modat@ucl.ac.uk
#Moloney, Brendan, AIRC, moloney.brendan@gmail.com
#Mongkolwat, Pattanasak, Northwestern U, p-mongkolwat@northwestern.edu
#Montillo, Albert, GE Research, montillo@ge.com
#Nagpal, Simrin, Queen’s Univ, 7sn6@cs.queensu.ca
#Nakhmani, Arie, BU, nakhmani@gmail.com
#Nardelli, Pietro, Univ College Cork, pie.nardelli@gmal.com
#Norton, Isaiah, BWH, inorton@partners.org
#Nouranian, Saman, Univ BC, samann@ece.ubc.ca
#O'Donnell, Lauren, BWH, odonnell@bwh.harvard.edu
#Oyama, Rie, BWH, royama@bwh.harvard.edu
#Paniagua, Beatriz, Univ NC Chapel Hill, bpaniagua@gmail.com
#Penzkofer, Tobias, SPL, pt@bwh.harvard.edu
#Pernelle, Guillaume, BWH, gpernelle@gmail.com
#Pieper, Steve, Isomics, pieper@bwh.harvard.edu
#Pinter, Csaba, Queen's Univ, pinter@cs.queensu.ca
#Pujol, Sonia, BWH, spujol@bwh.harvard.edu
#Rannou, Nicolas, Childrens Hospital, nicolas.rannou@childrens.harvard.edu
#Razzaque, Sharif, InnerOptic Technology, sharif@inneroptic.com
#Risholm, Petter, Harvard, pettri@bwh.harvard.edu
#San Jose, Raul, BWH, rjosest@bwh.harvard.edu
#Schroeder, William, Kitware, will.schroeder@kitware.com
#Shackleford, James, MGH, jshackleford@partners.org
#Sharp, Greg, MGH, gcsharp@partners.org
#Shusharina, Nadya, MGH, nshusharina@partners.org
#Sojoudi, Samira, Univ BC, samiras@ece.ubc.ca
#Spindler, Wolf, Fraunhofer MEVIS, wolf.spindler@mevis.fraunhofer.de
#Sridharan, Ramesh, MIT CSAIL, rameshvs@MIT.EDU
#State, Andrei, InnerOptic Technology, andrei@inneroptic.com
#Tiwari, Pallavi, Rutgers, pallavi.tiwar@gmail.com
#Toews, Matthew, BWH HMS, mt@bwh.harvard.edu
#Tokuda, Junichi, BWH, tokuda@bwh.harvard.edu
#Ungi, Tamas, Queen's Univ, ungi@cs.queensu.ca
#Van Tol, Markus, Univ Iowa, mvantol@engineering.uiowa.edu
#Vosburgh, Kirby, BWH, kirby@bwh.harvard.edu
#Wang, Bo, SCI, bowang@sci.utah.edu
#Wang, Kevin, Princess Margaret Hospital, kevin.wang@rmp.uhn.on.ca
#Wedlake, Chris, Robarts, cwedlake@robarts.ca
#Welch, David, Univ Iowa, david-welch@uiowa.edu
#Wells, William, HMS BWH, sw@bwh.harvard.edu
#Whitaker, Ross, SCI, whitaker@cs.utah.edu
#White, Phillip, BWH HMS, white@bwh.harvard.edu
#Yamada, Atsushi, BWH, ayamada@bwh.harvard.edu
#Yarmakovich, Alex, Isomics, alexy@bwh.harvard.edu

2012 Summer Project Week

2012-06-18T14:28:59Z

Rameshvs:

Back to [[Events]]

[[image:PW-MIT2012.png|200px]]

*'''Dates:''' June 18-22, 2012
*'''Location:''' MIT

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 18
!style="width:18%" |Tuesday, June 19
!style="width:18%" |Wednesday, June 20
!style="width:18%" |Thursday, June 21
!style="width:18%" |Friday, June 22
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''9am-10am:''' What's new in Slicer4 (Charts - Jim, DICOM - Steve) <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
<br>----------------------------------------<br>
'''10-11am''' Slicer4 Python Q&A <br>
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9am-11pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans)
[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
<br>----------------------------------------<br>
'''10am-12pm: <font color="#4020ff">Computation Core PIs: closed meeting with Ron:'''</font><br>
[[MIT_Project_Week_Rooms#Star|Star Room]]
|'''9am-12pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Summer Project Week Breakout Session:Slicer in Networked Environment|Slicer in Networked Environment]] (Junichi)
[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-4:30pm''' Slicer4 Extensions (JC) <br>
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''3-4pm:''' [[2012_Tutorial_Contest|Tutorial Contest Presentations]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''4-5pm:''' Breakout Session: DICOM, Networking, RT, Segmentations (Steve, Greg, Andras, Andre)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Star Room]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2012|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3-30pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Summer Project Week Breakout Session:Ultrasound|QtTesting]] (JC)
|'''1-3pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2012 Summer Project Week Breakout Session:Ultrasound|Ultrasound]] (Tamas)
[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

==Projects==

Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template THIS TEMPLATE] to create project pages for this event.

==='''Neurosurgery, Brain and Spine, Traumatic Brain Injury'''===

# [[Semiautomatic longitudinal segmentation of MR volumes in traumatic brain injury]] (Andrei Irimia, Micah Chambers, Bo Wang, Marcel Prastawa, Danielle Pace, Stephen Aylward, Jack van Horn, Guido Gerig)
# [[2012_Summer_Project_Week:4D_Segmentation_TBI|4D Segmentation of longitudinal MRI of TBI patients]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig)
# [[2012_Summer_Project_Week:Intraoperative_Tract_Detection | Intraoperative White Matter Tract Detection Module]] (Lauren O'Donnell, Isaiah Norton)
# [[2012_Summer_Project_Week:Ultrasound_Aberration_Correction | An Ultrasound-based Method for Aberration Correction in TCFUS]] (Jason White, Greg Clement)
# [[2012_Summer_Project_Week:TimeSeriesMonitoringIntracranialBones| Monitoring time series images of intracranial bones in meningioma ]] (Karl Diedrich)
# [[2012_Summer_Project_Week:Early_Dementia_Diagnostic |Early Dementia Diagnostic Tools]] (Marcel Koek, Sonia Pujol)
# [[2012_Summer_Project_Week:Radnostics |Spine Segmentation & Osteoporosis Detection In CT Imaging Studies]] (Anthony Blumfield, Ron Kikinis)

==='''Radiation Therapy'''===
#[[2012_Summer_Project_Week:Atlas_based_segmentation_for_head_and_neck| Atlas-based segmentation for head and neck]] (Amelia Arbisser, Nadya Shusharina, James Shackleford, Greg Sharp, Polina Golland)
#[[2012_Summer_Project_Week:Overlapping_structures| First class structure set support in Slicer]] (Greg Sharp, James Shackleford, Steve Pieper)
#[[2012_Summer_Project_Week:PlastimatchIntegration| Plastimatch loadable module]] (James Shackleford, Greg Sharp)
#[[2012_Summer_Project_Week:Deformable_Registration_for_Head_and_Neck| Deformable Registration for Head and Neck ]] (Ivan Kolesov, Greg Sharp, Yi Gao, Allen Tannenbaum)
#[[2012_Summer_Project_Week:SlicerRT| Radiotherapy extensions for Slicer 4]] (Andras Lasso, Csaba Pinter, Kevin Wang)

==='''Huntington's Disease'''===
# [[2012_Summer_Project_Week:DTIPrep|DTIPrep]] (David Welch, Hans Johnson)
# [[2012_Summer_Project_Week:ANTS Registation|ANTS Registation Module]] (David Welch, Hans Johnson)
# [[2012_Summer_Project_Week:Nipype Integration|Slicer/Nipype Integration]] (Hans Johnson)
# [[2012_Summer_Project_Week:DicomToNrrd|DicomToNrrdConverter Integration]] (Kent Williams)
# [[2012_Summer_Project_Week:4D shape analysis|4D shape analysis]] (James Fishbaugh, Marcel Prastawa, Guido Gerig)
# [[2012_Summer_Project_Week:DTI-Reg|DTI atlas based fiber analysis]] (Francois Budin)

==='''Atrial Fibrillation'''===
# [[2012_Summer_Project_Week:UtahCardiacRegistration|Cardiac MRI Registration Module]] (Alan Morris, Danny Perry, Josh Cates, Greg Gardner, Rob MacLeod)
# [[2012_Summer_Project_Week:UtahAutoScar|Automatic Left Atrial Scar Detection]] (Danny Perry, Alan Morris, Josh Cates, Rob MacLeod)
# [[2012_Summer_Project_Week:UtahInhomogeneity|MRI Inhomogeneity Correction Filter]] (Alan Morris, Eugene Kholmovski, Josh Cates, Danny Perry, Rob MacLeod)
# [[2012_Summer_Project_Week:VecReg|Vector-Valued Cardiac MRI Registration]] (Yi Gao, Josh Cates, Liang-Jia Zhu, Alan Morris, Danny Perry, Greg Gardner, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
# [[2012_Summer_Project_Week:RidgeExtractionAtrialWallSegmentation|Perceptual Ridge Extraction for Atrial Wall Segmentation in MRI]] (Arie Nakhmani, Allen Tannenbaum)

==='''Device Integration with Slicer and Image Guided Therapy'''===
# [[2012_Summer_Project_Week:SlicerWeb|Web Interface to Slicer 4]] (Steve Pieper)
# [[2012_Summer_Project_Week:OpenIGTLinkIF|Improvement of OpenIGTLink IF for Slicer 4]] (Junichi Tokuda)
# [[2012_Summer_Project_Week:LeanSlicer|Lean Slicer to facilitate regulatory approval]] (Andras Lasso, Chris Wedlake)
# [[2012_Summer_Project_Week:LiveUltrasound|Live Ultrasound]] (Tamas Ungi, Andinet Enquobahrie, Junichi Tokuda)
# [[2012_Summer_Project_Week:BK-PLUS_Integration|Integration of BK ProFocus US with Slicer via PLUS library]] (Andras Lasso, Andrey Fedorov, Isaiah Norton, Saman)
# [[2012_Summer_Project_Week:TransformRecorder|Transform Recorder]] (Simrin Nagpal, Tamas Ungi)
# [[2012_Summer_Project_Week:Open_source_electromagnetic_trackers_using OpenIGTLink|Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)
# [[2012_Summer_Project_Week:4D_Ultrasound_Slicer4|4D Ultrasound on Slicer4]] (Laurent Chauvin, Nobuhiko Hata)
# [[2012_Summer_Project_Week:Kinect4Slicer|Kinect4Slicer]] (Laurent Chauvin, Nobuhiko Hata)
# [[2012_Summer_Project_Week:Needle Tip Tracking | Needle Tip Tracking for complex MR images]] (Atsushi Yamada, Nobuhiko Hata) ('''[NH Note...we will be late for the project introduction]''').
# [[2012_Summer_Project_Week:iGyne|iGyne for Gynecological Cancer Brachytherapy]] (Xiaojun Chen, Jan Egger, Tina Kapur, Steve Pieper)
#[[2012_Summer_Project_Week:Interactive_Needle_Segmentation|Interactive Needle Segmentation for Gynecological Cancer Brachytherapy]] (Nabgha Farhat, Neha Agrawal, Jan Egger, Tina Kapur, Steve Pieper)
# [[2012_Summer_Project_Week:VertebraCTUSReg|Single Vertebra CT-US Registration]] (Samira Sojoudi, Saman Nouranian, Simrin Nagpal, Tamas Ungi, David Welch)
# [[2012_Summer_Project_Week:Fast Fiducial Registration|Fast Fiducial Registration Module]] (David Welch, Hans Johnson, Nicole Aucoin, Ron Kikinis)
# [[2012_Summer_Project_Week:SteeredRegistration|Steered Registration for Image Guided Therapy]] (Guillaume Pernelle BWH, Jan Egger, Tina Kapur, Steve Pieper, Jim Miller, Kunlin Cao)

==='''General Segmentation'''===
#[[2012_Summer_Project_Week:SemiAutomatedAirwaySegmentationfrom0.64mmLungCTDatasets|Semi-automated airway segmentation from 0.64mm lung CT datasets]] (Padraig Cantillon-Murphy, Raul San Jose Estepar, Pietro Nardelli)
#[[Loading and segmentation of histopathology imaging for radiological-pathological correlation]] (Andrey Fedorov, Tobias Penzkofer)
#[[2012_Summer_Project_Week:ABC_Slicer4|Porting ABC extension to Slicer 4]] (Marcel Prastawa, Bo Wang, Guido Gerig)
# [[2012_Summer_Project_Week:QuantitativePETImageAnalysisModule|Quantitative PET Image Analysis Module]] (Markus Van Tol)
#[[2012_Summer_Project_Week:SegmentationWithLabelFusion|Segmentation with Label Fusion]] (Ramesh Sridharan, Christian Wachinger, Polina Golland)

==='''General Registration'''===
# [[2012_Summer_Project_Week:NiftyReg|NiftyReg integration]] (Marc Modat, Sonia Pujol)
#[[2012_Summer_Project_Week:ElastixIntegration|Elastix integration]] (Stefan Klein, Sonia Pujol)
# [[2012_Summer_Project_Week:DTIRegistration| Highly Deformable DTI Registration for cases with large pathological variations]] (Aditya Gupta, Martin Styner, Matthew Toews)
# [[2012_Summer_Project_Week:DifficultRegistration| Registration of Difficult Images]] (Matthew Toews, Stefan Klein, Marc Modat, Aditya Gupta, Martin Styner, Petter Risholm, Dominik Meier, William Wells)

==='''Informatics'''===
# [[2012_Summer_Project_Week:AIM_for_QIN|Applicability of AIM to QIN use cases]] (Andrey Fedorov, Reinhard Beichel, Jayashree Kalpathy-Cramer, Pat Mongkolwat, Daniel Rubin)
# [[2012_Summer_Project_Week:Reporting|Reporting module]] (Andrey, Nicole, Steve, Ron, Pat)

==='''Infrastructure'''===

# [[2012_Summer_Project_Week:SelfTesting|Built-In Self-Testing (BIST) for Slicer]] (Steve, Julien, Jc, Sonia)
# [[2012_Summer_Project_Week:AnnotationModule|Annotation module redesign for Slicer]] (Nicole)
# [[2012_Summer_Project_Week:MultiVolumeSupport|Multivolume support]] (Andrey, Jim, Brendan Moloney)
# [[2012_Summer_Project_Week:PythonCLIandWidget|Python CLI modules (Demian, JC, Julien, Steve)]].
# [[2012_Summer_Project_Week:Charting|Charting]] (Jim)
# [[2012_Summer_Project_Week:SimpleITK Integration|SimpleITK Integration]] (Hans Johnson, Bradley Lowekamp)
# [[2012_Summer_Project_Week:GPUEditor|GPU Editor Effects]] (Steve, Jim)
# [[2012_Summer_Project_Week:XTK|XTK/WebGL Exporter]] (Daniel, Nicolas - Boston Children's Hospital)
# [[2012_Summer_Project_Week:EventOptimization|Callback/Events/Observation best practice + Performance bottleneck discussion (Julien, Steve,...)]]
# [[2012_Summer_Project_Week:XNATSlicerIntegration|XNAT/Slicer Integration]] (Sunil, Dan, Steve,...)
# [[2012_Summer_Project_Week:ITKv4 Integration|ITKv4 Integration]] (Hans Johnson, Julien Finet, Jim). See [http://www.na-mic.org/Bug/view.php?id=2007 #2007]
# [[2012_Summer_Project_Week:LongitudinalPETCTModule|Slicer Module for longitudinal analysis of PET-CT]] (Paul, Andriy, Ron, Markus,...)
# [[2012_Summer_Project_Week:Threat Modeling|Threat Modeling]] (JC, J2, Anthony)

==Background==

We are pleased to announce the 15th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 26th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [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], [http://www.cimit.org CIMIT], and OCAIRO. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

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

== Logistics ==
*'''Dates:''' June 18-22, 2012.
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please click [https://www.regonline.com/namic2012summerprojweek HERE] to do an on-line registration for the meeting that will allow you to pay by credit card. No checks will be accepted.
*'''Registration Fee:''' $300 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' No room blocks have been reserved in any area hotel. Please select a [http://web.mit.edu/institute-events/visitor/stay.html hotel of your choice] and make reservations as early as possible. Some area hotels are:
**marriott cambridge center
**marriott residence inn kendall square
**le meridien central square
**hotel marlowe cambridge
**royal sonesta hotel cambridge

== Preparation ==
# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 26, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 10, all participants to add a one line title of their project to #Projects
# By 3pm ET on Thursday June 7, all project leads to complete [[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 3pm on June 14: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)
## 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. XNAT/MIDAS). 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.)
## Where possible, 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. (Matt)
# 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.
## Projects to develop extension modules should be built against the latest Slicer4 trunk.

==Registrants==

Do not add your name to this list- it is maintained by the organizers based on your paid registration. ([http://www.regonline.com/Register/Checkin.aspx?EventID=1089602 Please click here to register.])

#Anderson, Peter, retired, traneus@verizon.net
#Arbisser, Amelia, MIT, arbisser@mit.edu
#Aucoin, Nicole, BWH, Nicole@bwh.harvard.edu
#Aylward, Stephen, Kitware, stephen.aylward@kitware.com
#Blevins, Scott, BWH, stblevins@gmail.com
#Blumfield, Anthony, Radnostics, Anthony.Blumfield@Radnostics.com
#Budin, Francis, NIRAL-UNC, fbudin@unc.edu
#Cao, Kunlin, GE Research, cao@ge.com
#Chambers, Micah, UCLA, micahcc@ucla.edu
#Chauvin, Laurent, SPL, lchauvin@bwh.harvard.edu
#Chen, Elvis, Robarts, chene@robarts.ca
#Chen, Xiaojun, SPL, xiaojun@bwh.harvard.edu
#Datar, Manasi, Utah SCI, datar@sci.utah.edu
#Diedrich, Karl, AZE R&D, karl.diedrich@azeresearch.com
#Egger, Jan, BWH, egger@bwh.harvard.edu
#Farhat, Nabgha, SPL, nfarhat@bwh.harvard.edu
#Fedorov, Andriy, BWH, fedorov@bwh.harvard.edu
#Fillion-Robin, Jean-Christophe, Kitware, jchris.fillionr@kitware.com
#Finet, Julien, Kitware, julien.finet@kitware.com
#Fishbaugh, James, SCI, jfishbau@sci.utah.edu
#Gao, Yi, BWH, gaoyi@bwh.harvard.edu
#Gardner, Greg, SCI, ggardner@sci.utah.edu
#Golland, Polina, MIT CSAIL, polina@csail.mit.edu
#Gouaillard, Alexandre, A*STAR, agouaillard@gmail.com
#Gupta, Aditya, NIRAL UNC, aditya_gupta@med.unc.edu
#Jagadeesan, Jayender, SPL, jayender@bwh.harvard.edu
#Johnson, Hans, Univ Iowa, hans-johnson@uiowa.edu
#Kalpathy-Cramer, Jayashree, MGH, kalpathy@nmr.mgh.harvard.edu
#Kapur, Tina, BWH HMS, tkapur@bwh.harvard.edu
#Kikinis, Ron, HMS, kikinis@bwh.harvard.edu
#Klein, Stefan, Erasmus MC, s.klein@erasmusmc.nl
#Kleper, Vladimir, Northwestern Univ, vkleper@northwestern.edu
#Koek, Marcel, Erasmus MC, m.koek@erasmusmc.nl
#Kolesov, Ivan, GA Tech, ivan.kolesov@gatech.edu
#Kumar, Sunil, Washington Univ St Louis, kumars@mir.wustl.edu
#Lasso, Andras, Queen's Univ, lasso@cs.queensu.ca
#Lowekamp, Bradley, NLM/NIH, bradley.lowekamp@nih.gov
#Macule, Raul, AZE R&D, raul.macule@azeresearch.com
#Mastrogiacomo, Katie, SPL, BWH, kmast@bwh.harvard.edu
#Meier, Dominik, BWH, meier@bwh.harvard.edu
#Mercea, Paul, SPL, pmercea@bwh.harvard.edu
#Miller, Jim, GE Research, millerjv@ge.com
#Mizutani, Tatsushi, Nagoya Univ, tatsushi0207@me.com
#Modat, Marc, Univ College London, m.modat@ucl.ac.uk
#Moloney, Brendan, AIRC, moloney.brendan@gmail.com
#Mongkolwat, Pattanasak, Northwestern U, p-mongkolwat@northwestern.edu
#Montillo, Albert, GE Research, montillo@ge.com
#Nagpal, Simrin, Queen’s Univ, 7sn6@cs.queensu.ca
#Nakhmani, Arie, BU, nakhmani@gmail.com
#Nardelli, Pietro, Univ College Cork, pie.nardelli@gmal.com
#Norton, Isaiah, BWH, inorton@partners.org
#Nouranian, Saman, Univ BC, samann@ece.ubc.ca
#O'Donnell, Lauren, BWH, odonnell@bwh.harvard.edu
#Oyama, Rie, BWH, royama@bwh.harvard.edu
#Paniagua, Beatriz, Univ NC Chapel Hill, bpaniagua@gmail.com
#Penzkofer, Tobias, SPL, pt@bwh.harvard.edu
#Pernelle, Guillaume, BWH, gpernelle@gmail.com
#Pieper, Steve, Isomics, pieper@bwh.harvard.edu
#Pinter, Csaba, Queen's Univ, pinter@cs.queensu.ca
#Pujol, Sonia, BWH, spujol@bwh.harvard.edu
#Rannou, Nicolas, Childrens Hospital, nicolas.rannou@childrens.harvard.edu
#Razzaque, Sharif, InnerOptic Technology, sharif@inneroptic.com
#Risholm, Petter, Harvard, pettri@bwh.harvard.edu
#San Jose, Raul, BWH, rjosest@bwh.harvard.edu
#Schroeder, William, Kitware, will.schroeder@kitware.com
#Shackleford, James, MGH, jshackleford@partners.org
#Sharp, Greg, MGH, gcsharp@partners.org
#Shusharina, Nadya, MGH, nshusharina@partners.org
#Sojoudi, Samira, Univ BC, samiras@ece.ubc.ca
#Spindler, Wolf, Fraunhofer MEVIS, wolf.spindler@mevis.fraunhofer.de
#Sridharan, Ramesh, MIT CSAIL, rameshvs@MIT.EDU
#State, Andrei, InnerOptic Technology, andrei@inneroptic.com
#Tiwari, Pallavi, Rutgers, pallavi.tiwar@gmail.com
#Toews, Matthew, BWH HMS, mt@bwh.harvard.edu
#Tokuda, Junichi, BWH, tokuda@bwh.harvard.edu
#Ungi, Tamas, Queen's Univ, ungi@cs.queensu.ca
#Van Tol, Markus, Univ Iowa, mvantol@engineering.uiowa.edu
#Vosburgh, Kirby, BWH, kirby@bwh.harvard.edu
#Wang, Bo, SCI, bowang@sci.utah.edu
#Wang, Kevin, Princess Margaret Hospital, kevin.wang@rmp.uhn.on.ca
#Wedlake, Chris, Robarts, cwedlake@robarts.ca
#Welch, David, Univ Iowa, david-welch@uiowa.edu
#Whitaker, Ross, SCI, whitaker@cs.utah.edu
#White, Phillip, BWH HMS, white@bwh.harvard.edu
#Yamada, Atsushi, BWH, ayamada@bwh.harvard.edu
#Yarmakovich, Alex, Isomics, alexy@bwh.harvard.edu

2012 Summer Project Week:SegmentationWithLabelFusion

2012-06-18T14:28:41Z

Rameshvs: Created page with '__NOTOC__ <gallery> Image:PW-MIT2012.png|Projects List </gallery> ==Key Investigators== * MIT CSAIL: Ramesh Sridharan, Christian Wachinger,…'

__NOTOC__
<gallery>
Image:PW-MIT2012.png|[[2012_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT CSAIL: Ramesh Sridharan, Christian Wachinger, Polina Golland

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

<h3>Objective</h3>
Our goal is to improve the performance of nonparametric atlas segmentation methods in cases where images align poorly. We will examine local deformations as a measure of quality of image registration for segmentation.

</div>

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

<h3>Approach, Plan</h3>

Our plan for the Project Week is to apply our method to MR and CT data, including the Head and Neck Cancer dataset.

</div>

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

<h3>Progress</h3>

</div>
</div>

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

2012 Summer Project Week:SemiAutomatedAirwaySegmentationfrom0.64mmLungCTDatasets

2012-06-18T14:28:07Z

Rameshvs: Undo revision 76294 by Rameshvs (Talk)

__NOTOC__
<gallery>
Image:PW-MIT2012.png|[[2012_Summer_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>

==Key Investigators==
* University College Cork: Padraig Cantillon-Murphy, Pietro Nardelli
* HMS/HSDM: Raul San Jose Estepar

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

<h3>Objective</h3>
We are currently developing a system for endoscopic lung biopsy which would allow access to peripheral nodules using electromagnetic steering by means of external coils. The goal of the project is to steer a catheter inside the lung using an electromagnetic field (provided by 3 electromagnetic coils) and to couple this electromagnetic navigation system with real-time image registration so that the physician can see a 3D reconstruction of where he is in the airway by means of a virtual endoscopy system (registered to a prior CT).

</div>

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

<h3>Approach, Plan</h3>

For the virtual endoscopy system we are using 3D Slicer to implement a new tool that should allow the physician to have a fast 3D reconstruction of the airway and to move inside it following the catheter.
The idea for the tool is to firstly develop an element of semi-automatic segmentation improving the already available Slicer’s Simple Region Growing Module in order to obtain a new Adaptive Threshold Region Growing Module capable of automatic increment. Then, by choosing the right threshold to reconstruct the airway as well as possible and to avoid leakage problems: for this purpose, we are planning to follow the Kiraly’s method [1], which seems a good trade-off between complexity and segmentation’s capability. In this way, we think to be able to obtain a first non-complete but sufficient for the most of the times segmentation and reconstruction of the airway,
Once the airway has been firstly segmented and reconstructed with the method above, the goal is to allow the physician to select the Region of Interest (ROI) (s)he wants to reach (such as a nodule inside a peripheral branch), and to implement a centerline extraction of the reconstructed airway, followed by a path decision, in order to obtain the right way to get the ROI: in this case, the Centerline Extraction Module developed by Estepar et al. for Airway Inspector (based on Slicer 2.8) [2] might be very helpful and we think to extend it to the new Slicer 4.
As the last segmentation part, we are planning to add (if possible) some new method to the Editor module in order to allow the physician, if necessary, to add and see new or existing branches of the airway, which the automatic segmentation can miss. The idea here is to allow the physician to improve the automatic segmentation described above for the purposes of having an optimized path to get to ROIs in deepest part of the lung.
Once the airway’s segmentation has been obtained and the lung has been reconstructed, we are planning to use (and possibly improve) the Slicer's Endoscopy and IGT Modules in order to move through the airway and to recognize and follow the catheter inside the lung.
We have a very nice collaboration in Cork with some interventional pulmonologists in the area of guidance for lung biopsy in cancer patients and the datasets we are using were generated by a 64-GE MEDICAL SYSTEM scanner at Cork University Hospital, with 2mm of slice thickness and a standard convolution kernel. We have also available the CT datasets which were used during EXACT 2009 and are freely available, as a benchmark for our approach against others existing .

</div>

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

<h3>Progress</h3>

</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 - No
##Extension -- commandline - Yes
##Extension -- loadable
#Other (Please specify)

==References==

1. Kiraly AP, Higgings WE, McLennan G, Hoffman EA, McLennan G, Reinhardt JM. Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. ''Acad Radiol'' 2002;9:1153-1168.

2. San Jose Estepar R, Washko GG, Silverman EK, Reilly JJ, Kikinis R, Westin CF. Airway inspector: An open source application for lung morphometry. In First International Workshop on Pulmonary Image Processing. New York City, USA, 2008;293-302.
</div>

2012 Summer Project Week:SemiAutomatedAirwaySegmentationfrom0.64mmLungCTDatasets

2012-06-18T14:26:17Z

Rameshvs:

2012 Summer Project Week

2012-05-14T07:24:16Z

Rameshvs: /* General Segmentation */

2012 Winter Project Week

2012-01-13T00:15:50Z

Rameshvs: swapped order of our projects

Back to [[Project Events]], [[Events]]
Back to [[Project Events]], [[AHM_2012]], [[Events]]

__NOTOC__
[[image:PW-SLC2012.png|300px]]

== Dates.Venue.Registration ==

Please [[AHM_2012#Dates_Venue_Registration|click here for Dates, Venue, and Registration]] for this event.

== [[AHM_2012#Agenda|'''AGENDA''']] and Project List==

Please:
* [[AHM_2012#Agenda|'''Click here for the agenda for AHM 2012 and Project Week''']].
* [[#Projects|'''Click here to jump to Project list''']]

==Background==

From January 9-13, 2012, the 14th project week for hands-on research and development activity in Neuroscience and Image-Guided Therapy 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==

===Traumatic Brain Injury ===

* [[2012_Winter_Project_Week:TBIClinicalAnalysis|Segmentation of Serial MRI of TBI patients
using Personalized Atlas Construction]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIDTIAnalysis|Registration and analysis of white matter tract changes in TBI]] (Clement Vachet, Anuja Sharma, Marcel Prastawa, Andrei Irimia, Jack van Horn, Guido Gerig, Martin Styner, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIValidation|Validation, visualization and analysis of segmentation for TBI]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Jack van Horn, Guido Gerig, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:GeometricMetamorphosisTBI|Geometric Metamorphosis for TBI]] (Danielle Pace, Marc Niethammer, Marcel Prastawa, Andrei Irimia, Jack van Horn, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIRegistration|Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units]] (Yifei Lou, Andrei Irimia, Patricio Vela, Allen Tannenbaum, Micah C. Chambers, Jack Van Horn and Paul M. Vespa, Danielle Pace, Stephen Aylward)
* [[2012_Winter_Project_Week:TBIRegistration|Integration of unscented Kalman filter (UKF) based multi-tensor tractography in Slicer]] (Christian Baumgartner, Yogesh Rathi, Carl-Fredrik Westin)

===Predict Huntington's Disease===
* [[2012_Winter_Project_Week:SPIEWorkshop|SPIE DTI Workshop Preparation: Perform DTI Quality Control]] (Jean-Baptiste Berger, Sonia Pujol, Guido Gerig, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:DWIPhantom|DTI tractography phantom: a software for evaluating tractography algorithms]] (Gwendoline Roger,Yundi Shi, Clement Vachet, Martin Styner, Sylvain Gouttard)
* [[2012_Winter_Project_Week:FVLight|FiberViewerLight: a fiber bundle visualization and clustering tool]] (Jean-Baptiste Berger, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:DTIAFA|DTIAtlasFiberAnalyzer]] (Jean-Baptiste Berger, Yundi Shi, Clement Vachet, Martin Styner)
* [[2012_Winter_Project_Week:PairWiseDTIRegistration|Pairwise DTI registration: DTI-Reg]] (Clement Vachet, Hans Johnson, Martin Styner)
* [[2012_Winter_Project_Week:ShapeAnalysisSubcorticalStructuresHD|Morphometric analysis in subcortical structures in HD]] (Beatriz Paniagua, Clement Vachet, Hans Johnson, Martin Styner)
* [[2012_Winter_Project_Week:DTI pipeline|Applying our DTI pipeline to analyse HD data]] (Gopalkrishna Veni, Hans Johnson, Martin Styner, Ross Whitaker)
* [[2012_Winter_Project_Week: DTI Change Modeling | Longitudinal change modeling of fiber tracts in serial HD DTI data]] (Anuja Sharma, Hans Johnson, Guido Gerig)
* [[2012_Winter_Project_Week: Continuous 4D shapes | Continuous 4d shape models from time-discrete data: Subcortical structures in HD]] (James Fishbaugh, Hans Johnson, Guido Gerig)

===Atrial fibrillation ===
* [[2012_Winter_Project_Week:EndoSeg|Endocardial Segmentation in DE-MRI for AFib]] (Yi Gao, Liang-Jia Zhu, Josh Cates, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:LAWallRegistration|Longitudinal Alignment and Visualization of Left-Atrial Wall from DEMRI and MRA]] (Josh Cates, Yi Gao, Liang-Jia Zhu, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:PVRegistration|Longitudinal Alignment and Visualization of Pulmonary Veins from DEMRI and MRA]] (Josh Cates, Yi Gao, Liang-Jia Zhu, Greg Gardner, Alan Morris, Danny Perry, Rob MacLeod, Sylvain Bouix, Allen Tannenbaum)
* [[2012_Winter_Project_Week:RealTime|OpenIGT for realtime MRI-guided RF ablation]] (Rob MacLeod, and Junichi Tokuda)
* [[2012_Winter_Project_Week:GraphbasedSeg|Graph based segmentation on LGE-MRI data]] (Gopal Veni, Ross Whitaker)

===Head and Neck Cancer ===
* [[2012_Winter_Project_Week:PairwiseLF|Label fusion with pairwise interactions]] (Ramesh Sridharan, Christian Wachinger, Polina Golland)
* [[2012_Winter_Project_Week:PatchBased|A patch-based approach to the segmentation of organs of risk]] (Christian Wachinger, Polina Golland)
* [[RT dose comparison tool for Slicer]] (Nadya Shusharina, Greg Sharp)
* [[2012_Winter_Project_Week:InteractiveSegmentation|Interactive editing tools for segmentation]] (Greg Sharp, Steve Pieper)
* [[2012_Winter_Project_Week:UserInTheLoop_InteractiveSegmn|Interactive 3D Level-Set Segmentation]] (Peter Karasev, Karl Fritscher, Ivan Kolesov, Allen Tannenbaum)
* [[2012_Winter_Project_Week:GBMseg|Segmentation of polymodal images of GBM's in Slicer]] (Misha Milchenko, Dan Marcus, Andrey Fedorov, Jan Egger, Isaiah Norton, Jayashree)

===IGT for Surgery and Radiation Treatments===
*[[2012_Winter_Project_Week:OpenIGTLink_Interface_for_Slicer4| OpenIGTLink interface for Slicer4]] (Junichi Tokuda, Clif Burdette/Jack Blevins, Tamas Ungi, Andras Lasso)
*[[2012_Winter_Project_Week:LiveUltrasound|Live ultrasound in Slicer4 using Plus and OpenIGTLink]] (Tamas Ungi, Elvis Chen, Junichi Tokuda)
*[[2012_Winter_Project_Week:4DUltrasound|4D Ultrasound Storage and Volume Rendering on Slicer 3.6]] (Laurent, Noby)
*[[2012_Winter_Project_Week:BKPLUSSlicer|Integration of BK Ultrasound into PLUS and Slicer]] (Mehdi Moradi, Isaiah Norton, Tamas Ungi)
*[[2012_Winter_Project_Week:LaserAblationAndGuidance|Slicer-BrainLab integration for laser ablation]] (Erol Yeniaras, Isaiah Norton, Pratik Patel)
*[[2012_Winter_Project_Week:PelvicRegistration|Deformable prostate registration: 3D ultrasound to MRI]] (Mehdi Moradi, Jan Egger, Andrey Fedorov)
*[[2012_Winter_Project_Week:iGyne | iGyne: A Software Prototype to support Gynecologic Radiation Treatment in AMIGO]] (Jan Egger, Xiaojun Chen, Radhika Tibrewal, Mehdi Moradi, Antonio Damato, Kanokpis Townamchai, Tina Kapur, Akila Viswanathan)
*[[2012_Winter_Project_Week:hybridMRS | Generation of a hybrid MR-Spectroscopic (MRS) dataset under 3DSlicer for Neurosurgery]] (Jan Egger, Isaiah Norton, Bjoern Menze, Daniel Hořínek, Antonín Škoch, Jens Sommer, Christopher Nimsky, Alexandra Golby, Tina Kapur)
*[[2012_Winter_Project_Week:Needle Detection in MR Images for Brachytherapy in AMIGO|Needle Detection in MR Images for Brachytherapy in AMIGO]] (Radhika Tibrewal, Jan Egger, Xiaojun Chen, Matthew Toews, Stephen Aylward)
*[[2012_Winter_Project_Week:Needle Detection in MR Images for Brachytherapy in AMIGO|Needle Detection in MR Images for Brachytherapy in AMIGO]]
*[[2012_Winter_Project_Week:RTTools|RT tools for Slicer4]] (Csaba Pinter, Kevin Wang, Andras Lasso, Greg Sharp)
*[[2012_Winter_Project_Week:RTSS|RT structure set data representation]] (Greg Sharp, Andras Lasso, Steve Pieper, etc.)

===Musculoskeletal System===
* [[2012_Winter_Project_Week:Radnostics|Spine Segmentation & Osteoporosis Screening In CT Imaging Studies]] (Anthony Blumfield)

===Registration===
* [[2012_Winter_Project_Week:CMFreg|Framework for Cranio-Maxillo Facial registration in Slicer3]] (Beatriz Paniagua, Lucia Cevidanes, Martin Styner)
* [[2012_Winter_Project_Week:SlidingOrgans|Registration in the presence of sliding between organs (Danielle Pace, Marc Neithammer, Stephen Aylward)]]
* [[2012_Winter_Project_Week:GeometricMetamorphosis|Estimating the infiltration / recession of pathologies independent of background deformations (Danielle Pace, Stephen Aylward, Marc Niethammer)]]
* [[2012_Winter_Project_Week:FastInterpolation|Fast Image Interpolation Given Parameterized Deformations For Image Registration (Ivan Kolesov, Greg Sharp, Allen Tannenbaum)]]
* [[2012_Winter_Project_Week:CTLiverRegistration|Register liver CT images for tumor progress monitoring]] (Karl Diedrich, Nobuhiko Hata, Atsushi Yamada)
* [[2012_Winter_Project_Week:Fast Multi-modal Registration|Fast Registration with User-guidance for AMIGO]] (Dave Welch, Hans Johnson, Nicole Aucoin, Ron Kikinis)

===Shape Analysis===
* [[2012_Winter_Project_Week:PNSnormals|Principal Nested Spheres Normal Consistency in ShapeWorks]] (Beatriz Paniagua, Josh Cates, Manasi Datar, Ross Whitaker, Martin Styner)
* [[2012_Winter_Project_Week:GeomIndicesSlicer4|Porting of White Matter Geometric Indices Module to Slicer4]] (Peter Savadjiev)
* [[2012_Winter_Project_Week:ParticleWrapper|Slicer end-to-end particle correspondence wrapper module]] (Ipek Oguz, Beatriz Paniagua, Josh Cates, Manasi Datar, Ross Whitaker, Martin Styner)

===NA-MIC Kit Internals===
*Slicer4 release (Jean-Christophe Fillion-Robin (JC), and Julien Finet (J2))
*Slicer4 extensions (JC)
*Slicer4 documentation (JC)
*Slicer4 GUI Testing (Benjamin Long, JC, J2)
*Slicer4 data on MIDAS (Josh Cates, Patrick Reynolds)
*[[2012_Project_Week:SceneViews|Slicer4 Scene Views Module]] (Nicole Aucoin, Ron Kikinis, Julien Finet)
*[[2012_Project_Week:AnnotationsFileFormatRefactor|Annotations Module File Format Refactor]] (Nicole Aucoin)
*[[2012_Project_Week:QT3DTextRendering|QT 3D Text rendering proof of concept]] (Julien Finet, Steve Pieper, Nicole Aucoin)
*[[2012_Project_Week:DICOM|DICOM Networking, Database, and Slicer Integration]] (Steve, Andrey, Andras)
*[[2012_Project_Week:EditorExtensions|Editor Extension Examples and Debugging]] (Steve, Andrey, Jc, Hans, Satra)
*[[2012_Project_Week:GeneralGUI|General minor GUI redesign]] (Wendy Plezniak, Julien Finet, Ron Kikinis)
*[[2012_Project_Week:ViewerControls|Redesign of the slice viewer control panels]] (Julien Finet, Ron Kikinis, Hans Johnson, Greg Sharp)
*[[2012_Project_Week:AutomatedTesting |Automated Testing (Sonia Pujol, Steve Pieper, Jc, Benjamin)]]
*[[2012_Project_Week:RemoveSlicerLegacyCode|Remove legacy code from slicer4 (itk, modules, build scripts) (Hans, Jim, Steve, J2, JC)]]
*[[2012_Project_Week:BatchProcessing|Batch Processing with Slicer Modules]] (Steve, Andrey, JC, Hans, Satra, Dave Welch)
*[[2012_Project_Week:4DImageSlicer4|Support for 4D Images in Slicer4]] (Andrey, Steve, Junichi, Alex)
*[[2012_Project_Week:QIN-SAM|QIN Slicer Annotation Module: AIM, DICOM SR and Slicer annotations]] (Andrey, Steve, Nicole, Jayashree)

=== 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 27th, 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 27: MGH DBP
#*November 3: Iowa DBP Huntingtons, Engineering Infrastructure Topics
#*November 10: Utah Atrial Fibrillation DBP
#*November 17: UCLA TBI DBP
#*November 24: No call. thanksgiving.
#*December 1:
#*December 8:
#*December 15:Finalize Projects
#*January 5: Loose Ends
#By December 15: [[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 15: 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. MIDAS, xNAT). 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...

2012 Winter Project Week:PairwiseLF

2012-01-13T00:14:51Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Investigators ==
* Ramesh Sridharan
* Christian Wachinger
* Polina Golland

<div style="margin: 20px;">

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
Our goal is to improve the performance of label fusion for high-variability datasets in which registration algorithms may not align subjects very well. We will investigate two directions for improving label fusion:
* We will examine the potential of using parameters learned over the label fusion training set to improve the quality of segmentation
* We will examine pairwise interactions between images in the training set for label fusion.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
Our approach is to learn intensity and label prediction parameters over pairs of images in the training set.

Our plan for the Project Week is to apply our method to MR and CT data, including the Head and Neck Cancer dataset.

</div>

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

<h3>Progress</h3>
* Discussed data and motivation with fellow project week participants
* Discussed registration issues with data
* Continued to apply method to synthetic data
</div>

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

2012 Winter Project Week:PairwiseLF

2012-01-09T20:22:45Z

Rameshvs:

2012 Winter Project Week:PairwiseLF

2012-01-05T21:00:26Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-SLC2012.png|[[2012_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Investigators ==
* Ramesh Sridharan
* Christian Wachinger
* Polina Golland

<div style="margin: 20px;">

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Objective</h3>
Our goal is to improve the performance of label fusion for high-variability datasets in which registration algorithms may not align subjects very well. We will investigate two directions for improving label fusion:
* We will examine the potential of using parameters learned over the label fusion training set to improve the quality of segmentation
* We will examine pairwise interactions between images in the training set for label fusion.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<h3>Approach, Plan</h3>
Our approach is to learn intensity and label prediction parameters over pairs of images in the training set.

Our plan for the Project Week is to apply our method to the Head and Neck Cancer and Atrial Fibrillation datasets.

</div>

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

<h3>Progress</h3>
*

</div>

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

Algorithm:MIT

2011-10-28T18:20:18Z

Rameshvs: /* Quantitative Susceptibility Mapping */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

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

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

<font color="red">'''New: '''</font> B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

<font color="red">'''New: '''</font> Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. In Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

<font color="red">'''New: '''</font> G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

<font color="red">'''New: '''</font> G. Langs, B.H. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, 56(2):497-507, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

<font color="red">'''New: '''</font> A. Dalca, G. Danagoulian, R. Kikinis, E. Schmidt, and P. Golland. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6893:537, 2011.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

|-

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

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

|-

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

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

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

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

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

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

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

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

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

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

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

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

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

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T18:19:47Z

Rameshvs: /* Quantitative Susceptibility Mapping */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

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

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

<font color="red">'''New: '''</font> B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

<font color="red">'''New: '''</font> Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

<font color="red">'''New: '''</font> G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

<font color="red">'''New: '''</font> G. Langs, B.H. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, 56(2):497-507, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

<font color="red">'''New: '''</font> A. Dalca, G. Danagoulian, R. Kikinis, E. Schmidt, and P. Golland. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6893:537, 2011.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

|-

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

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

|-

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

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

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

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

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

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

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

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

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

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

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

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

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

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:47:50Z

Rameshvs: /* Segmentation and Visualization for Cardiac Ablation Procedures */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

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

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

<font color="red">'''New: '''</font> B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

<font color="red">'''New: '''</font> Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

<font color="red">'''New: '''</font> G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

<font color="red">'''New: '''</font> G. Langs, B.H. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, 56(2):497-507, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

<font color="red">'''New: '''</font> A. Dalca, G. Danagoulian, R. Kikinis, E. Schmidt, and P. Golland. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6893:537, 2011.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

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

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

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

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

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

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

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

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

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

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

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

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

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

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

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

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:47:35Z

Rameshvs: /* Nonparametric Models for Supervised Image Segmentation */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

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

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

<font color="red">'''New: '''</font> M. Depa, G. Holmvang, E.J. Schmidt, P. Golland and M.R. Sabuncu. Towards Efficient Label Fusion by Pre-Alignment of Training Data. In Proc. MICCAI Workshop on Multi-atlas Labeling and Statistical Fusion, 38-46, 2011.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

<font color="red">'''New: '''</font> B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

<font color="red">'''New: '''</font> Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

<font color="red">'''New: '''</font> G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

<font color="red">'''New: '''</font> G. Langs, B.H. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, 56(2):497-507, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

<font color="red">'''New: '''</font> A. Dalca, G. Danagoulian, R. Kikinis, E. Schmidt, and P. Golland. Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6893:537, 2011.

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

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

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

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

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

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

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

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

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

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

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

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

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

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

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

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

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

Algorithm:MIT

2011-10-28T17:34:14Z

Rameshvs: /* Brain Tumor Segmentation and Modeling */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of MIT Algorithms (PI: Polina Golland) =

Our group seeks to model statistical variability of anatomy and function across subjects and between populations and to utilize computational models of such variability to improve predictions for individual subjects, as well as characterize populations. Our long-term goal is to develop methods for joint modeling of anatomy and function and to apply them in clinical and scientific studies. We work primarily with anatomical, DTI and fMRI images. We actively contribute implementations of our algorithms to the NAMIC-kit.

= MIT Projects =

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

|| [[Image:Segmentation_example2.png|250px]]
||

== [[Projects:NonparametricSegmentation| Nonparametric Models for Supervised Image Segmentation]] ==

We propose a non-parametric, probabilistic model for the automatic segmentation of medical images,
given a training set of images and corresponding label maps. The resulting inference algorithms we
develop rely on pairwise registrations between the test image and individual training images. The
training labels are then transferred to the test image and fused to compute a final segmentation of
the test subject. [[Projects:NonparametricSegmentation|More...]]

M.R. Sabuncu, B.T.T Yeo, K. Van Leemput, B. Fischl, and P. Golland. A Generative Model for Image Segmentation Based on Label Fusion. IEEE Transactions on Medical Imaging, 29(10):1714-1729, 2010.

|-

|| [[Image:Mdepa_scar_DE-MRI_projection.png| 250px]]
||

== [[Projects:CardiacAblation | Segmentation and Visualization for Cardiac Ablation Procedures]] ==

Catheter radio-frequency (RF) ablation is a technique used to treat atrial fibrillation, a very common heart condition. The objective of this project is to provide automatic segmentation and visualization tools to aid in the planning and outcome evaluation of cardiac ablation procedures. Specifically, we develop methods for the automatic segmentation of the left atrium of the heart and visualization of the ablation scars resulting from the procedure in clinical MR images.
[[Projects:CardiacAblation|More...]]

<font color="red">'''New: '''</font> M. Depa, M.R. Sabuncu, G. Holmvang, R. Nezafat, E.J. Schmidt, and P. Golland. Robust Atlas-Based Segmentation of Highly Variable Anatomy: Left Atrium Segmentation. In Proc. of MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function, LNCS 6364:85-94, 2010.

|-

| | [[Image:TGIt.gif|center| 150px]]
| |

== [[Projects:LatentAtlasSegmentation|Joint Segmentation of Image Ensembles via Latent Atlases]] ==

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. The atlases are typically generated by averaging manual labels of aligned brain regions across different subjects. However, the availability of comprehensive, reliable and suitable manual segmentations is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas.
[[Projects:LatentAtlasSegmentation|More...]]

T. Riklin Raviv, K. Van-Leemput, B.M. Menze, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via Latent Atlases, Medical Image Analysis (MedIA), 14(5):654-665, 2010.

|-

| | [[Image:BjoernTumor3.png‎|center|200px]]
| |

== [[Projects:TumorModeling|Brain Tumor Segmentation and Modeling]] ==

We are interested in developing computational methods for the assimilation of magnetic resonance image data into physiological models of glioma - the most frequent primary brain tumor - for a patient-adaptive modeling of tumor growth. [[Projects:TumorModeling|More...]]

<font color="red">'''New: '''</font> B. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M.A. Weber, N. Ayache and P.A. Golland. A generative approach for image-based modeling of tumor growth. To appear in Proc. IPMI: Information Processing in Medical Imaging, 2011.

|-

| | [[Image:NerveSegRes1.jpg|center| 200px]]
| |

== [[Projects:NerveSegmentation|Segmentation of Nerve and Nerve Ganglia in the Spine]] ==
Automatic segmentation of neural tracts in the dural sac and outside of the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves in high resolution MR images makes segmentation a challenging task. [[Projects:NerveSegmentation|More...]]

|-

|| [[Image: JointVar_Functional_p05_2.jpg|center| 150px]]
| |

== [[Projects:BrainConnectivity|Brain Connectivity]] ==
Our goal is to use measures of connectivity between various ROIs as an avenue for understanding the structural and functional organization of the brain. We assess functional and anatomical connectivity using both fMRI correlations and DWI tractography measures, respectively. [[Projects:BrainConnectivity|More...]]

A. Venkataraman, Y. Rathi, M. Kubicki, C-F. Westin and P. Golland. Joint Generative Model for fMRI/DWI and its Application to Population Studies. In Proc. MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervenion, LNCS 6361:191-199, 2010.

A. Venkataraman, M. Kubicki, C.-F. Westin, and P. Golland. Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

| | [[Image:Namic wiki.png|200px]]
| |

== [[Projects:QuantitativeSusceptibilityMapping| Quantitative Susceptibility Mapping ]] ==

There is increasing evidence that excessive iron deposition in specific regions
of the brain is associated with neurodegenerative disorders such as Alzheimer's
and Parkinson's disease. The role of iron in the pathogenesis of these diseases
remains unknown and is difficult to determine without a non-invasive method
to quantify its concentration in-vivo. Since iron is a ferromagnetic substance,
changes in iron concentration result in local changes in the magnetic susceptibility of tissue.
In magnetic resonance imaging (MRI) experiments, differences
in magnetic susceptibility cause perturbations in the local magnetic field, which
can be computed from the phase of the MR signal.[[Projects:QuantitativeSusceptibilityMapping|More...]]

<font color="red">'''New: '''</font> Poynton C., Wells W. A Variational Approach to Susceptibility Estimation That is Insensitive to B0 Inhomogeneity. To appear in Proc. ISMRM: International Society of Magnetic Resonance in Medicine, 2011.

|-

| | [[Image:Atlas_OneCluster.png|center| 200px]]
| |

== [[Projects:ConnectivityAtlas| Functional connectivity atlases and tumors]] ==
We learn an atlas of the functional connectivity structure that emerges during a cognitive process from a group of individuals. The atlas is a group-wise generative model that describes the fMRI responses of all subjects in the embedding space. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution.
[[Projects:ConnectivityAtlas|More...]]

<font color="red">'''New: '''</font> G. Langs, D. Lashkari, A. Sweet, Y. Tie, L. Rigolo, A.J. Golby, and P. Golland. Learning an Atlas of a Cognitive Process via Functional Geometry. In Proc. IPMI: International Conference on Information Processing and Medical Imaging, 6801:135-146, 2011.

G. Langs, Y. Tie, L. Rigolo, A. Golby, and P. Golland. Functional Geometry Alignment and Localization of Brain Areas. In Advances in Neural Information Processing Systems (NIPS), 2010.

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:lh.pm14686.BA2.gif|250px]]
||

== [[Projects:LearningRegistrationCostFunctions| Learning Task-Optimal Registration Cost Functions]] ==

We present a framework for learning the parameters of registration cost functions. The parameters of the registration cost function -- for example, the tradeoff between the image similarity and regularization terms -- are typically determined manually through inspection of the image alignment and then fixed for all applications. We propose a principled approach to learn these parameters with respect to particular applications. [[Projects:LearningRegistrationCostFunctions|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, D. Holt, K. Amunts, K. Zilles, P. Golland, and B. Fischl. Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex. IEEE Transactions on Medical Imaging, 29(7):1424-1441, 2010.

|-

| | [[Image:CoordinateChart.png|250px]]
| |

== [[Projects:SphericalDemons|Spherical Demons: Fast Surface Registration]] ==

We present the fast Spherical Demons algorithm for registering two spherical images. By exploiting spherical vector spline interpolation theory, we show that a large class of regularizers for the modified demons objective function can be efficiently approximated on the sphere using convolution. Based on the one parameter subgroups of diffeomorphisms, [[Projects:SphericalDemons|More...]]

B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl and P. Golland. Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, 29(3):650-668, 2010.

|-

| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|200px]]
| |

== [[Projects:fMRIClustering|Improving fMRI Analysis using Supervised and Unsupervised Learning]] ==

One of the major goals in the analysis of fMRI data is the detection of networks in the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements. [[Projects:fMRIClustering|More...]]

D. Lashkari, R. Sridharan, and P. Golland. Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations. In Advances in Neural Information Processing Systems 23, 1252--1260, 2010.

D. Lashkari, E. Vul, N.G. Kanwisher, and P. Golland. Discovering structure in the space of fMRI selectivity profiles. NeuroImage, 3(15):1085-1098, 2010.

D. Lashkari, R. Sridharan, E. Vul, P.-J. Hsieh, N. Kanwisher, and P. Golland. Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. In Proc. MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2010.

|-

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

== [[Projects:fMRIDetection|fMRI Detection and Analysis]] ==

We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Projects:fMRIDetection|More...]]

W. Ou, W.M. Wells III, and P. Golland. Combining Spatial Priors and Anatomical Information for fMRI Detection. Medical Image Analysis, 14(3):318-331, 2010.

|-

| | [[Image:GiniContrast_Icon.png|center| 150px]]
| |

== [[Projects:GiniContrast| Multi-variate activation detection]] ==
We study and demonstrate the benefits of Random Forest classifiers and the associated Gini importance measure for selecting voxel subsets that form a mul- tivariate neural response. The method does not rely on a priori assumptions about the signal distribution, a specific statistical or functional model or regularization. Instead it uses the predictive power of features to characterize their relevance for encoding task information.
[[Projects:GiniContrast|More...]]

<font color="red">'''New: '''</font> G. Langs, G. Menze, D. Lashkari, and P. Golland. Detecting Stable Distributed Patterns of Brain Activation Using Gini Contrast. NeuroImage, in press, 2011.

|-

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

== [[Projects:FieldmapFreeDistortionCorrection|Fieldmap-Free EPI Distortion Correction]] ==

In this project we aim to improve the EPI distortion correction algorithms. [[Projects:FieldmapFreeDistortionCorrection|More...]]

|-

{| cellpadding="10" style="text-align:left;"
|| [[Image:TetrahedralAtlasWarp.gif‎ |250px]]
||

== [[Projects:BayesianMRSegmentation| Bayesian Segmentation of MRI Images]] ==

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. [[Projects:BayesianMRSegmentation|More...]]

|-

| | [[Image:ICluster_templates.gif|250px]]
| |

== [[Projects:MultimodalAtlas|Multimodal Atlas]] ==

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called '''iCluster''', is used to compute multiple atlases for a given population.
[[Projects:MultimodalAtlas|More...]]

|-

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

== [[Projects:GroupwiseRegistration|Groupwise Registration]] ==

We extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment.

In a related project, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates. [[Projects:GroupwiseRegistration|More...]]

|-

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

== [[Projects:RegistrationRegularization|Optimal Atlas Regularization in Image Segmentation]] ==

We propose a unified framework for computing atlases from manually labeled data sets at various degrees of “sharpness” and the joint registration and segmentation of a new brain with these atlases. Using this framework, we investigate the tradeoff between warp regularization and image ﬁdelity, i.e. the smoothness of the new subject warp and the sharpness of the atlas in a segmentation application.
[[Projects:RegistrationRegularization|More...]]

|-

| | [[Image:FoldingSpeedDetection.png|150px|]]
| |

== [[Projects:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development [[Projects:ShapeAnalysisWithOvercompleteWavelets|More...]]

|-

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

== [[Projects:DTIModeling|Fiber Tract Modeling, Clustering, and Quantitative Analysis]] ==

The goal of this work is to model the shape of the fiber bundles and use this model description in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|More...]]

|-

| | [[Image:Progress_Registration_Segmentation_Shape.jpg|180px]]
| |

== [[Projects:ShapeBasedSegmentationAndRegistration|Shape Based Segmentation and Registration]] ==

This type of algorithm assigns a tissue type to each voxel in the volume. Incorporating prior shape information biases the label assignment towards contiguous regions that are consistent with the shape model. [[Projects:ShapeBasedSegmentationAndRegistration|More...]]

|-

| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|200px]]
| |

== [[Projects:DTIFiberRegistration|Joint Registration and Segmentation of DWI Fiber Tractography]] ==

The goal of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. [[Projects:DTIFiberRegistration|More...]]

|-

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

== [[Projects:DTIClustering|DTI Fiber Clustering and Fiber-Based Analysis]] ==

The goal of this project is to provide structural description of the white matter architecture as a partition into coherent fiber bundles and clusters, and to use these bundles for quantitative measurement. [[Projects:DTIClustering|More...]]

|-

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

== [[Projects:DTISegmentation|DTI-based Segmentation]] ==

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Projects:DTISegmentation|More...]]

|-

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

== [[Projects:DTIStochasticTractography|Stochastic Tractography]] ==

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. [[Projects:DTIStochasticTractography|More...]]

|-

| | [[Image:HippocampalShapeDifferences.gif|200px]]
| |

== [[Projects:ShapeAnalysis|Population Analysis of Anatomical Variability]] ==

Our goal is to develop mathematical approaches to modeling anatomical variability within and across populations using tools like local shape descriptors of specific regions of interest and global constellation descriptors of multiple ROI's. [[Projects:ShapeAnalysis|More...]]

|}

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-24T14:10:00Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
Image:adni_embedding2.png|Learned embedding from part of ADNI data
</gallery>
'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
</div>

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

<h3>Approach, Plan</h3>
#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
#:This is a relatively new project, and we are interested in hearing about potential applications and useful directions for the work.

</div>

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

<h3>Progress</h3>

#:Applied method to part of the ADNI dataset
#:Obtained HD data for future analysis


</div>
</div>

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

==References==
Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

==Delivery Mechanism==

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-24T13:54:18Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
Image:adni_embedding2.png|Learned embedding from part of ADNI data
</gallery>
'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
</div>

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

<h3>Approach, Plan</h3>
#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
#:This is a relatively new project, and we are interested in hearing about potential applications and useful directions for the work.

</div>

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

<h3>Progress</h3>

#:Applied baseline method to part of the ADNI dataset
#:Obtained HD data for future analysis


</div>
</div>

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

==References==
Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

==Delivery Mechanism==

File:Adni embedding2.png

2011-06-24T13:53:28Z

Rameshvs:

File:Adni embedding.png

2011-06-24T13:52:14Z

Rameshvs:

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-24T13:50:58Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
Image:adni_embedding.png|Learned embedding from part of ADNI data
</gallery>
[[File:[[File:Example.jpg]][[File:[[File:Example.jpg]][[File:[[File:Example.jpg]]]]]]]]
'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
</div>

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

<h3>Approach, Plan</h3>
#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
#:This is a relatively new project, and we are interested in hearing about potential applications and useful directions for the work.

</div>

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

<h3>Progress</h3>

#:Applied baseline method to part of the ADNI dataset
#:Obtained HD data for future analysis


</div>
</div>

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

==References==
Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

==Delivery Mechanism==

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-20T05:55:52Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
</gallery>

'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
</div>

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

<h3>Approach, Plan</h3>
#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
#:This is a relatively new project, and we are interested in hearing about potential applications and useful directions for the work.

</div>

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

<h3>Progress</h3>



</div>
</div>

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

==References==
Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

==Delivery Mechanism==

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-20T05:32:23Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
</gallery>

'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
</div>

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

<h3>Approach, Plan</h3>
#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
#:This is a relatively new project.

</div>

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

<h3>Progress</h3>



</div>
</div>

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

==References==
Belkin, M; Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

==Delivery Mechanism==

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-20T05:30:28Z

Rameshvs:

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
</gallery>

'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand pathology (e.g. Alzheimer's, Huntington's) in brain images.
</div>

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

<h3>Approach, Plan</h3>
#:We want to learn better embeddings of brain images (to better perform classification, segmentation/registration, etc). We will use a modification of spectral embedding techniques that allows us to incorporate constraints. For example, when longitudinal data involving progression of some pathology is available, we would like to incorporate our knowledge about the temporal relationship by constraining the longitudinal images to line up.
#:This is a relatively new project.

</div>

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

<h3>Progress</h3>



</div>
</div>

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

==References==

==Delivery Mechanism==

2011 Summer Project Week

2011-06-17T18:08:16Z

Rameshvs:

Back to [[Events]]

[[image:PW-MIT2011.png|right|200px]]

*'''Dates:''' June 20-24, 2011
*'''Location:''' MIT

==Preliminary Agenda==
==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 20
!style="width:18%" |Tuesday, June 21
!style="width:18%" |Wednesday, June 22
!style="width:18%" |Thursday, June 23
!style="width:18%" |Friday, June 24
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''9am-11am:''' [[2011 Project Week Breakout Session: Slicer4|Slicer 4 Core Modules Usability Review]]''' [[MIT_Project_Week_Rooms#Star|Star Room]]
<br>----------------------------------------<br>
'''11-12pm: <font color="#4020ff">Breakout Session:'''</font> [[2011 Summer Project Week Breakout Session Slicer4 Annotation|Slicer4 Annotations]] (Nicole Aucoin)<br>
[[MIT_Project_Week_Rooms#Star|Star Room]]
|'''9am-12pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2011 Project Week Breakout Session: ITK|ITK]] (Luis Ibanez)
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''9am-4pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2011 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: <font color="#503020">Ron Kikinis: Welcome</font>'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3:30-5:00pm: NA-MIC Kit Update''' Slicer4 Developers Guided Tour (Pieper) ([[media:2011 Summer-Slicer4.ppt|Draft Slides]]), Slicer4 Extension Writing Tutorial (Finet, Fillion-Robin)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''1-3pm: <font color="#4020ff">Breakout Session:'''</font><br> [http://wiki.slicer.org/slicerWiki/index.php/Slicer4:MultiVolumeContainer#Summer_2011_Project_Week_Breakout_Session Slicer4 MultiVolume Containers] (Nicole Aucoin)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Star Room]]
<br>----------------------------------------<br>
'''3-4pm:''' [[Summer_2011_Tutorial_Contest|Tutorial Contest Presentations]]
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''4-5pm:''' [[2011 Summer Project Week Breakout Session VTKCharts|VTK Charts]] (Marcus Hanwell)
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Star Room]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2011|Tutorial Contest Winner Announcement]]
<br>[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
<br>----------------------------------------<br>
'''3-4pm: <font color="#4020ff">Breakout Session:'''</font><br>[[2011_Summer_Project_Week_Breakout_Session_EMRegistration|Inter-subject Registration for EM segmenter]]
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|'''1-4pm: <font color="#4020ff">Breakout Session:'''</font><br> [[2011 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
<br>[[MIT_Project_Week_Rooms#Kiva_32-G449|Kiva Room]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

==Projects==

* Please make a link for your project to a new page based on the [[2011_Summer_Project_Week_Template| 2011 Summer Project Page Template]]

#[[2011_Summer_Project_Week_Slicer_DICOM_RT_Brachytherapy|Visualization of DICOM RT Planning Contours and Dose Distributions for Prostate and Gynecologic Brachytherapy in Slicer]](Tina Kapur, Greg Sharp, Jan Egger, Firdaus Janoos)
#Visualization of b-spline and vector fields (Steve, Danielle, Dominik)
#[[2011_Summer_Project_Week_Annotation_Module|Annotation Module in Slicer4]] (Nicole Aucoin, Daniel Haehn)
#[[2011_Summer_Project_Week_RECIST|RECIST Slicer4 module]] (Nicole Aucoin)
#[[2011_Summer_Project_Week_Dicom2Nrrd|DicomToNrrdConverter refactoring]] ( Xiaodong Tao, Mark Scully)
#[[2011_Summer_Project_Week_normal_consistency_particles|Normal consistency in particle correspondence computation using great circles in principal spheres - Huntington's Disease]], (Beatriz Paniagua, Martin Styner, Sungkyu Jung, Mark Scully)
#[[2011_Summer_Project__Week_Shape_Analysis_UNC |SPHARM & particles shape analysis - Huntington's Disease]] - Lucile Bompard, Clement Vachet, Beatriz Paniagua, Martin Styner
#Non-rigid, inter-patient registration of bone masks derived from CT for Head and Neck Cancer Radiation Therapy (Ivan Kolesov, Yi Gao, Gregory Sharp, and Allen Tannenbaum)
#[[2011_Summer_Project_Week_RSS_for_AFib_Ablation|Robust Statistical Segmentation (RSS) for the Atrial Fibrillation Ablation Therapy]] (Yi Gao, Kedar Patwardhan, Wassim Haddad, and Allen Tannenbaum, Rob MacLeod, Josh Blauer, and Josh Cates)
#[[Multimodality Image Registration for TBI]] (Yifei Lou, Danielle Pace, Jack Van Horn?, Marcel Prastawa?)
#[[2011_Summer_Project_Week_Segmentation_TBI|Segmentation of Longitudinal TBI data]] (Bo Wang, Jack Van Horn, Andrei Irimia, Marcel Prastawa, Guido Gerig)
#Longitudinal Shape Regression - Huntington's Disease (James Fishbaugh, Guido Gerig)
#[[2011_Summer_Project__Week_DVH|Dose volume histograms in Slicer]] (Greg Sharp, Nadya Shusharina, Steve Pieper, Csaba Pinter, Tina Kapur)
#[[2011_Summer_Project__Week_DICOM_RT|Synthetic images, vector fields, RT structures and RT doses in Slicer and ITK]]. (Nadya Shusharina, Greg Sharp, Luis Ibanez, Steve Pieper)
#[[2011_Summer_Project_Week_Watersheds|Interactive Watersheds Segmentation Module for Slicer for Atrial Fibrillation and HN Cancer]] (Josh Cates, Ross Whitaker, Steve Pieper, Jim Miller, Nadya)
#[[2011_Summer_Project_Week_NerveSeg|Segmentation of Nerve and Nerve Ganglia in the Spine]] (Adrian Dalca, Giovanna Danagoulian, Ron Kikinis, Ehud Schmidt, Polina Golland)
#[[2011_Summer_Project_Week_Image_Manifold_Learning_with_Spectral_Embedding_and_Laplacian_Eigenmaps|Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps]] (Ramesh Sridharan, Polina Golland)
#Shapeworks Shape Analysis for Atrial Fibrilliation and HD (Manasi Datar, Beatriz UNC, Mark Scully)
#Explore the applicability of RSS and Shapeworks for Ventricular Segmentation(Chiara Carminati, ?, ?)
#[[2011_Summer_Project_Week_Integrate_BRAINSCut_into_Slicer3]](Regina Kim, ... )
#[[2011_Summer_Project_Week_SlicerVmtk4|The Vascular Modeling Toolkit in 3D Slicer 4]] (Daniel Haehn, Luca Antiga, Steve Pieper, Kilian Pohl, Ron Kikinis)
#[[2011_Summer_Project_Week__LANDWARP_integration|Integration of LANDWARP into interactive registration module]] (Andrey Fedorov, Greg Sharp, Nadya Shusharina)
#[[2011_Summer_Project_Week_Registration_of_mouse_brains|Registration of mouse brains]] (Francois Budin)
#ShapeWorks Applications (Manasi Datar, Beatriz Paniagua, Martin Styner, Ross Whitaker, ?)
#[[2011_Summer_Project__Week_Wireless_Joystick|Wireless joystick controlling medical devices and software (e.g. Slicer4) in the operating room]] (Szymon Kostrzewski)
#[[2011_Summer_Project__Week_Live_Tracked_Ultrasound|Live Tracked Ultrasound with Slicer4 (Tamas Heffter)]]
#[http://wiki.na-mic.org/Wiki/index.php/Survey_stealthlink_openigtlink Surveying research teams interested in Open IGT Link support of Stealth Station (Nobuhiko Hata, Ron Kikinis)]
#[[2011_Summer_Project_Week_DTIPrep|DTIPrep - "Study-specific Protocol" based automatic DWI/DTI quality control and preparation]] - Huntington's Disease (Mashid Farzinfar, Clement Vachet, Joy Matsui, Martin Styner)
#[[2011_Summer_Project_Week_DTI_PairWise_Registration|DTI pair-wise registration module]] - Huntington's Disease (Clement Vachet, Joy Matsui, Mark Scully, Martin Styner)
#Volumetric DTI into Slicer for HD for Tract based roi segmentation (Steve Callahan, Mark Scully, Jim Miller)
#Nifti Support for Diffusion Tensor Images (Demian)
#Finishing details on the workflows: DICOM->Full brain tractography / peritumoral (Demian)
#[[2011_Summer_Project_Week_Slicer4_Tractography_Interaction_and_Display|Tractography Bundle/Cluster Interaction and Display in Slicer4]] (Lauren, Isaiah, Demian)
#[[2011_Summer_Project_Week_White_Matter_Laterality|White Matter Laterality in Python/Slicer4]] (Lauren)
#[[Summer_project_week_2011_Finsler_Streamlines|Adding streamlined tractography to the Finsler front propagation tractography toolkit]] (Antonio Tristán-Vega)
#[[Summer_project_week_2011_Workflows_SOA|Workflows and Service Oriented Architecture Modules for Slicer4 as Extensions]] (Alexander Zaitsev, Wendy Plesniak, Ron Kikinis)
#[[2011_Summer_Project__Week_DICOM_Networking|DICOM Networking interface for Slicer4]] (Steve Pieper, Nicole Aucoin, Noby Hata)
#[[2011_Summer_Project__Week_Stenosis_Detector|Stenosis Detector in 3D Slicer 4]] (Suares Tamekue, Daniel Haehn, Luca Antiga)
#[[2011_Summer_Project_Week_Spine_Segmentation_And_Osteoporosis_Screening_CT|Spine Segmentation & Osteoporosis Screening In CT Imaging Studies]] (Anthony Blumfield)
#Slicer module for building an average population HARDI Atlas (Ryan Eckbo)
#[[2011_Summer_Project_Week_4DUltrasound_HybridProbe_OsteoPlan|4D Ultrasound / Hybrid Probe / OsteoPlan]] (Laurent Chauvin, Noby Hata)
#[[2011_Summer_Project_Week_EMSegmentation_in_3D_Slicer4|EM Segmentation in 3D Slicer 4]] (Daniel Haehn, Dominique Belhachemi, Kilian Pohl)
#[[NonRigidRegistrationThatAccommodatesResection|Demons Based Non-Rigid Registration that Accommodates Resection in 3D Slicer]] (Petter Risholm, Sandy Wells)
#[[2011_Summer_Project_Week_re-parameterize_fiber|Re-parameterize fiber tracts for fiber statistics analysis]]
#[[2011_Summer_Project_Week_Automated_GUI_Testing| Automated GUI Testing (Sonia Pujol, Steve Pieper, Dave Partyka, Jean-Christophe Fillion-Robin, Xiaodong Tao)]]
#[[2011_Summer_Project_Week_Plastimatch_for_EMSegmenter | Integrating Plastimatch into the EMSegmenter]] (Dominique Belhachemi, Kilian Pohl, Greg Sharp)
#[[2011_Summer_Project_Week_Customizing_EMSegmenter_pipelines_for_brain_lesions | Customizing EMSegmenter pipelines for brain lesions]] (Dominique Belhachemi, Alexander Zaitsev, Kilian Pohl)
#[[2011_Summer_Project_Week_Slicer_Extension_for_GLISTR | Slicer extension for GLiome Image SegmenTation and Registration (GLISTR)]] (Andreas Schuh, Daniel Haehn, Kilian Pohl)
#[[2011_Summer_Project_Week_WMGeometry_Slicer4 | White matter geometry measures in Slicer 4]] (Peter Savadjiev)
#[[2011_Summer_Project_Week_Internationalization_of_Slicer|Internationalization of Slicer]] (Luping Fang, Steve Pieper, Daniel Haehn, Suares Tamekue, Jean-Christophe Fillion-Robin, Julien Finet, Yiming Ge, Ping Cao)
#[[2011_Summer_Project_Week__BRAINSFit_new_features_integration|Integrate new features into BRAINSFit]] (Andrey Fedorov, Hans Johnson, Mark Scully)
#[[2011_Summer_Project_Week_FetchMI:_Slicer_integration_with_XNAT |FetchMI: Slicer integration with XNAT 1.5]] (Misha Milchenko, Wendy Plesniak)
#[[2011_Summer_Project_Week_ODF_though_Fiber_Counting | ODF computation through fiber counting]] (Yinpeng Li, Ipek Oguz, Martin Styner)
#[[2011_Summer_Project_Week_Intraoperative_Brain_Shift_Monitor|Intraoperative Brain Shift Monitor]] (Jason White, Alex Golby, Steve Pieper)
#[[2011_Summer_Project_Week_DTI_Volumetric_Segmentation_for_Group_studies | DTI Volumetric Segmentation for Group studies]] (Gopal Veni, Ross Whitaker)
#[[2011_Summer_Project_Week_Segementation_Reconstruction_Pericardial_Sac]] (Mohasen)
#[[2011_Summer_Project_Week_Needle_Detection_to_Control_Scanner_for_Prostate_Biopsy | Needle Detection to Control Scanner for Prostate Biopsy ]] (Atsushi Yamada, Loïc Cadour, Junichi Tokuda and Nobuhiko Hata)

==Background==

We are pleased to announce the 13th PROJECT WEEK of hands-on research and development activity for applications in Image-Guided Therapy, Neuroscience, and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 28th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [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]. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

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

== Logistics ==
*'''Dates:''' June 20-24, 2011
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please register [http://guest.cvent.com/d/sdqy0l/4W here]. Payment must be made by credit card.
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' Boston Marriott Cambridge, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. Group rate is $199/night plus tax. Book [http://www.marriott.com/hotels/travel/boscb?groupCode=jrbjrba&app=resvlink&fromDate=6/19/11&toDate=6/24/11 here] or call 1-617-494-6600 and mention that you are booking in the MIT Room Block. '''All reservations must be made by May 29, 2011 to receive the discounted rate.'''

== Preparation ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing infrastructure projects in a kickoff TCON on April 28, 3pm ET. In the weeks following, new and old participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 12, all participants to add a one line title of their project to #Projects
#By 3pm ET on Thursday June 9, all project leads to complete [[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 3pm on June 16: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
## 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. XNAT/MIDAS). 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## Projects to develop extension modules should work with the [http://viewvc.slicer.org/viewcvs.cgi/branches/Slicer-3-6/#dirlist Slicer-3-6 branch] (new code should not be checked into the branch).
## Projects to modify core behavior of slicer should be done on the [http://viewvc.slicer.org/viewcvs.cgi/trunk/ trunk].

==Registrants==

Do not add your name to this list- it is maintaining by the organizers based on your paid registration (see Logistics for link)
# Nicole Aucoin , Brigham and Women's Hospital
# Dominique Belhachemi , University of Pennsylvania
# Anthony Blumfiled , Radnostics
# Lucile Bompard , UNC NIRAL
# Sylvain Bouix , Brigham and Women's Hospital
# Francois Budin , UNC NIRAL
# Steve Callahan , University of Utah
# Joshua Cates , University of Utah
# Laurent Chauvin , Brigham and Women's Hospital
# Adrian Dalca , MIT CSAIL
# Manasi Datar , University of Utah
# Colin Davey ,
# Ryan Eckbo , Brigham and Women's Hospital
# Jan Egger , University Hospital of Marburg
# Luping Fang , Zhejiang University of Technology, China
# mahshid farzinfar , UNC
# Andriy Fedorov , Brigham and Women's Hospital
# Julien Finet , Kitware Inc.
# James Fishbaugh , University of Utah
# Greg Gardner , University of Utah
# Ronen Globinsky , Yale University
# Maged Goubran , Robarts Research Institute
# Daniel Haehn , University of Pennsylvania
# Mike Halle , Brigham and Women's Hospital
# Noby Hata , Brigham and Women's Hospital
# Tamas Heffter , Queen's University
# Andrei Irimia , University of California, Los Angeles
# Hans Johnson , University of Iowa
# Ilknur Kabul , Kitware
# Tina Kapur , Brigham and Women's Hospital
# Ron Kikinis , Brigham and Women's Hospital; Harvard Medical School
# Regina Kim , University of Iowa
# Szymon Kostrzewski , Ecole Polytechnique Federale de Lausanne EPFL
# Dillon Lee , University of Utah
# Yinpeng Li , UNC-NIRAL
# Yifei Lou , Georgia Institute of Technology
# mohsen mahvash , Harvard Medical School (BWH and VA)
# Katie Mastrogiacomo , Brigham and Women's Hospital
# Joy Matsui , University of Iowa
# Dominik Meier , BWH
# Mikhail Milchenko , Washington University in St. Louis
# James Miller , GE Research
# Isaiah Norton , Brigham and Women's Hospital
# Danielle Pace , Kitware
# Beatriz Paniagua , University of North Carolina at Chapel Hill
# Xenophon Papademetris , Yale University
# Kedar Patwardhan , GE Global Research
# Steve Pieper , Isomics, Inc.
# Csaba Pinter , Queen's University
# Wendy Plesniak , Brigham and Women's Hospital
# Kilian Pohl , UPenn
# Marcel Prastawa , University of Utah
# Sonia Pujol , Brigham and Women's Hospital
# Martin Rajchl , Robarts Research Institute
# Petter Risholm , Brigham and Women's Hospital
# Peter Savadjiev , Brigham and Women's Hospital
# Andreas Schuh , University of Pennsylvania
# Mark Scully , University of Iowa
# Gregory Sharp , MGH
# Yundi Shi , UNC-Chapel Hill
# Nadya Shusharina , MGH
# Ramesh Sridharan , MIT CSAIL
# Hao Su , WPI
# Suarez Tamekue , Brigham and Women's Hospital
# Xiaodong Tao , GE Research
# Clement Vachet , UNC Chapel Hill
# Antonio Vega , Brigham and Women's Hospital
# Gopal Veni , University of Utah
# Bo Wang , University of Utah
# Demian Wasserman , Brigham and Women's Hospital
# Sandy Wells , Brigham and Women's Hospital
# Jason White , Brigham and Women's Hospital
# Atsushi Yamada , Brigham and Women's Hospital
# Alexander Yarmarkovich , Isomics
# Alexander Zaitsev , Brigham and Women's Hospital

2011 Summer Project Week Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps

2011-06-17T18:07:04Z

Rameshvs: Created page with '__NOTOC__ <gallery> Image:PW-MIT2011.png|Projects List Image:notfound.png|Interesting picture to be added... </gallery> '''Image Manifold …'

__NOTOC__
<gallery>
Image:PW-MIT2011.png|[[2011_Summer_Project_Week#Projects|Projects List]]
Image:notfound.png|Interesting picture to be added...
</gallery>

'''Image Manifold Learning with Spectral Embedding and Laplacian Eigenmaps'''

==Key Investigators==
* MIT: Ramesh Sridharan
* MIT: Polina Golland

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

<h3>Objective</h3>

#:Use image manifold learning to better understand
#:pathology (e.g. Alzheimer's, Huntington's)
#: Use embedding to improve segmentation, etc

</div>

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

<h3>Approach, Plan</h3>
#:Impose constraints on learned manifold to incorporate longitudinal
#:data, etc.

</div>

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

<h3>Progress</h3>



</div>
</div>

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

==References==

==Delivery Mechanism==

2011 Summer Project Week

2011-05-19T18:27:30Z

Rameshvs:

Projects:fMRIClustering

2011-03-30T22:29:22Z

Rameshvs:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Improving fMRI Analysis using Supervised and Unsupervised Learning =

One of the major goals in the analysis of fMRI data is the detection of regions of the brain with similar functional behavior. A wide variety of methods including hypothesis-driven statistical tests, supervised, and unsupervised learning methods have been employed to find these networks. In this project, we develop novel learning algorithms that enable more efficient inferences from fMRI measurements.

= Clustering for Discovering Structure in the Space of Functional Selectivity =

We are devising clustering algorithms for discovering structure in the functional organization of the high-level visual cortex. It is suggested that there are regions in the visual cortex with high selectivity to certain categories of visual stimuli; we refer to these regions as /functional units/. Currently, the conventional method for detection of these regions is based on statistical tests comparing response of each voxel in the brain to different visual categories to see if it shows considerably higher activation to one category. For example, the well-known FFA (Fusiform Face Area) is the set of voxels which show high activation to face images. We use a model-based clustering approach to the analysis of this type of data as a means to make this analysis automatic and further discover new structures in the high-level visual cortex.

We formulate a model-based clustering algorithm that simultaneously
finds a set of activation profiles and their spatial maps from fMRI time courses. We validate
our method on data from studies of category selectivity in the visual
cortex, demonstrating good agreement with findings from prior
hypothesis-driven methods. This hierarchical model enables functional group analysis
independent of spatial correspondence among subjects. We have also developed a co-clustering extension of this
algorithm which can simultaneously find a set of clusters of voxels and categories
of stimuli in experiments with diverse sets of stimulus categories. Our model is nonparametric, learning the numbers of clusters in both domains as well as the cluster parameters.

Fig. 1 shows the categories learned by our algorithm on a study with 8 subjects. We split trials of each image into two groups of equal size and consider
each group as an independent stimulus forming a total of 138
stimuli. Hence, we can examine the consistency of our stimulus categorization with respect to identical trials. Stimulus pairs
corresponding to the same image are generally assigned to the same
category, confirming the consistency of the resuls across
trials. Category 1 corresponds to the scene images and, interestingly, also includes all images of
trees. This may suggest a high level category structure that is not
merely driven by low level features. Such a structure is even more
evident in the 4th category where images of a tiger that has a large
face join human faces. Some other animals are clustered together with human bodies in categories 2 and
9. Shoes and cars, which have similar shapes, are clustered together
in category 3 while tools are mainly found in category 6.

{|
|+ '''Fig 1. Categories learned from 8 subjects'''
|align="center"|[[Image:Category_singlefile1.png |thumb|800px]]
|}

Fig. 2 shows the cluster centers, or activation profiles, for the first 13 of 25 clusters learned by our method. We see salient category structure in our profiles. For instance, system 1 shows lower responses to cars, shoes, and tools compared to other stimuli. Since the images representing these three categories in our experiment are generally smaller in terms of pixel size, this system appears selective to lower level features (note that the highest probability of activation among shoes corresponds to the largest shoe 3). System 3 and system 8 seem less responsive to faces compared to all other stimuli.

{|
|+ '''Fig 2. System profiles of posterior probabilities of activation for each system to different stimuli. The bar heights correspond to the posterior probability of activation.'''
|align="center"|[[Image:Hdpprofs_all_1.png |thumb|800px]]
|}

Fig. 3 shows the membership maps for the systems 2, 9, and 12, selective for bodies, faces, and scenes, respectively, which our model learns in a completely unsupervised fashion from the data. For comparison, Fig. 4 shows the significance maps found by applying the conventional confirmatory t-test to the data from the same subject. While significance maps appear to be generally larger than the extent of systems identified by our method, a close inspection reveals that system membership maps include the peak voxels for their corresponding contrasts.

{|
|+ '''Fig 3. Membership probability maps corresponding to systems 22, 9, and 12, selective respectively for bodies (magenta), scenes (yellow), and faces (cyan) in one subject.'''
|align="center"|[[Image:Sys_2_9_12_subj1.png |thumb|800px]]
|}

{|
|+ '''Fig 4. Map representing significance values for three contrasts: bodies-objects (magenta), faces-objects (cyan), and scenes-objects (yellow) in the same subject. Lighter colors correspond to higher significance.'''
|align="center"|[[Image:Sys_2_9_12_subj1.png |thumb|800px]]
|}

'''''Earlier work'''''

Fig. 5 compares the map of voxels assigned to a face-selective profile by an earlier version of our algorithm with the t-test's map of voxels with statistically significant (p<0.0001) response to faces when compared with object stimuli. Note that in contrast with the hypothesis testing method, we don't specify the existence of a face-selective region in our algorithm and the algorithm automatically discovers such a profile of activation in the data.

{|
|+ '''Fig 5. Spatial maps of the face selective regions found by the statistical test (red) and our mixture model (dark blue). Maps are presented in alternating rows for comparison. Visually responsive mask of voxels used in our experiment is illustrated in yellow and light blue.'''
|align="center"|[[Image:mit_fmri_clustering_mapffacompare.PNG |thumb|800px]]
|}

'''''Hierarchical Model for Exploratory fMRI Analysis without Spatial Normalization'''''

Building on the work on the clustering model for the domain specificity, we develop a hierarchical exploratory method for simultaneous parcellation of multisub ect fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to ﬁt the model to the data. The resulting algorithm ﬁnds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on the same visual fMRI study as before. Fig. 6 shows the scene-selective parcel in 2 different subjects. Parcel-level spatial correspondence is evident in the figure between the subjects.

<table>
<tr> <th> '''Fig 6. The map of the scene selective parcels in two different subjects. The rough location of the scene-selective areas PPA and TOS, identified by the expert, are shown on the maps by yellow and green circles, respectively.'''
<tr>
<td align="center">
[[Image:mit_fmriclustering_hierarchicalppamapsubject1.jpg |650px]]
<td align="center">
[[Image:mit_fmriclustering_hierarchicalppamapsubject2.jpg |650px]]
</table>

= Key Investigators =

* MIT: Danial Lashkari, Archana Venkataraman, Ed Vul, Nancy Kanwisher, Polina Golland.
* Harvard: J. Oh, Marek Kubicki, Carl-Fredrik Westin.

= Publications =

[http://www.na-mic.org/publications/pages/display?search=Projects%3AfMRIClustering&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on fMRI clustering]

Project Week Results: [[2008_Summer_Project_Week:fMRIconnectivity|June 2008]]

[[Category:fMRI]]

File:Sigs subj1.png

2011-03-30T22:22:31Z

Rameshvs: significance values for fMRI clustering (t-test)

significance values for fMRI clustering (t-test)