NAMIC Wiki - User contributions [en]

2010 Summer Project Week Shape

2010-12-13T21:58:24Z

Tammy: /* Project */

Projects:TumorModeling

2010-10-21T02:02:45Z

Tammy: /* Key Investigators */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Modeling tumor growth in patients with glioma =
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.

This aims at two directions: First, it aims at making complex information from longitudinal multimodal data set accessible for diagnostic radiology through physiological models. This will allow to estimate features such as degree of infiltration, speed of growth, or mass effect in a quantitative fashion; for therapy it will allow to identify regions at risk for progression. Second, it aims at providing the means to test different mavroscopic tumor models from theoretical biology on real clinical data.

To realize these aims, the project comprises a number of ascpects -- automated segmentation of tumors in large multimodal image data sets, making information of different MR image modalities accessible for the tumor model, with a focus on the processing of magnetic resonance spectroscopic images (MRSI), and the development of methods for the image-based estimation of parameters in reaction-diffusion type models of tumor growth.

[[Image:Multimodal_glioma.png|thumb|center|600px| Figure 1: Multi-modal image data from a patient with low-grade glioma. A large number of different modalities and derived parameter volumes are acquired during the monitoring of tumor growth.]]

== Segmenting tumors in large multimodal data sets ==
To segment all MR image volumes available for a patient we developed an approach for learning patient-specific lesion atlases (Figure 2) with limited user interaction. Figure 2 shows the manual segmentation of the tumor from different raters (red, green, blue) and the automatic segmentation using the patient-specific lesion atlas (black) in T1-MRI, T1-MRI and the fractional anisotropy map from DTI.

[[Image:Tumor_segmentation_lesion_atlas.png|thumb|center|600px| Figure 2: Tumor segmentation - by human rater (red, green, blue) and our methods (black). The right image shows the lesion atlas.]]

== Processing magnetic resonance spectroscopic images ==
To make the metabolic information of magnetic resonance spectroscopic images available for modeling the evolution of glioma growth we are implementing an [http://wiki.na-mic.org/Wiki/index.php/2009_Summer_Project_Week_MRSI-Module MRSI processing module] for Slicer.

= Key Investigators =
* MIT: [http://people.csail.mit.edu/menze Bjoern Menze], [http://people.csail.mit.edu/tammy Tammy Riklin Raviv], [http://people.csail.mit.edu/koen Koen Van Leemput], [http://people.csail.mit.edu/polina Polina Golland]
* Harvard: William M. Wells
* INRIA Sophia-Antipolis, France: Ezequiel Geremia, Olivier Clatz, Nicholas Ayache
* DKFZ Heidelberg, Germany: Bram Stieltjes, Marc-Andre Weber

= Publications =
*[http://www.na-mic.org/publications/pages/display?search=Projects%3ATumorModeling&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on brain tumor segmentation and modeling]

2010 Summer Project Week Shape

2010-06-25T14:12:56Z

Tammy: /* Project */

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT: Tammy Riklin Raviv
* BWH: Sylvain Bouix

==Project==
<div style="margin: 10px;">
<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Objective</h3>
Group-wise statistical analysis of brain structures is significant for clinical and neuroanatomy studies. Nevertheless, due to the inherent difficulty to properly represent the morphology of the structures of interest, the statistics is usually limited to volume and surface area measurements. Ideally, we would require an injective shape representation that is invariant rigid or even affine transformations and form a vector space. In the absence of shape representation that meets all of these requirements, it is difficult to define fundamental concepts such as the median or average shape.

We defined a novel measure for shape dissimilarity that is based on shape boundaries. This measure allows us to construct the 'median' of shapes of the same class that provides reacher morphological characteristics of the ensemble. Our goal in this project is to apply the proposed method to a population study of brain structures and test its discriminative power.
</div>

<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Approach, Plan</h3>
We use a variational framework based on level-sets to deform a representative (canonical) shape such that the sum of its distances from each of the shapes in the group is minimized.
We will first test the approach on a set of synthetic shapes where the resulting median shape can be validated. The outcomes of this preliminary stage are expected to guide us in planning more advanced experiments.
</div>

<div style="width: 35%; float: left;">
<h3>Progress</h3>
We made progress in the process of transforming the Matlab code into ITK.
Specifically we worked on building a new ITK boundary-based shape metric and incorporating it into our registration framework prior to the construction
of the boundary-based median shape.

</div>

2010 Summer Project Week Shape

2010-06-25T14:11:28Z

Tammy: /* Project */

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT: Tammy Riklin Raviv
* BWH: Sylvain Bouix

==Project==
<div style="margin: 10px;">
<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Objective</h3>
Group-wise statistical analysis of brain structures is significant for clinical and neuroanatomy studies. Nevertheless, due to the inherent difficulty to properly represent the morphology of the structures of interest, the statistics is usually limited to volume and surface area measurements. Ideally, we would require an injective shape representation that is invariant rigid or even affine transformations and form a vector space. In the absence of shape representation that meets all of these requirements, it is difficult to define fundamental concepts such as the median or average shape.

We defined a novel measure for shape dissimilarity that is based on shape boundaries. This measure allows us to construct the 'median' of shapes of the same class that provides reacher morphological characteristics of the ensemble. Our goal in this project is to apply the proposed method to a population study of brain structures and test its discriminative power.
</div>

<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Approach, Plan</h3>
We use a variational framework based on level-sets to deform a representative (canonical) shape such that the sum of its distances from each of the shapes in the group is minimized.
We will first test the approach on a set of synthetic shapes where the resulting median shape can be validated. The outcomes of this preliminary stage are expected to guide us in planning more advanced experiments.
</div>

<div style="width: 35%; float: left;">
<h3>Progress</h3>
We made progress in the process of transforming the Matlab code into ITK.
Specifically we worked on building a new ITK boundary based metric and incorporating it into our registration framework prior to the construction
of the boundary-based median shape.

</div>

2010 Summer Project Week Shape

2010-06-25T14:09:45Z

Tammy: /* Project */

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT: Tammy Riklin Raviv
* BWH: Sylvain Bouix

==Project==
<div style="margin: 10px;">
<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Objective</h3>
Group-wise statistical analysis of brain structures is significant for clinical and neuroanatomy studies. Nevertheless, due to the inherent difficulty to properly represent the morphology of the structures of interest, the statistics is usually limited to volume and surface area measurements. Ideally, we would require an injective shape representation that is invariant rigid or even affine transformations and form a vector space. In the absence of shape representation that meets all of these requirements, it is difficult to define fundamental concepts such as the median or average shape.

We defined a novel measure for shape dissimilarity that is based on shape boundaries. This measure allows us to construct the 'median' of shapes of the same class that provides reacher morphological characteristics of the ensemble. Our goal in this project is to apply the proposed method to a population study of brain structures and test its discriminative power.
</div>

<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Approach, Plan</h3>
We use a variational framework based on level-sets to deform a representative (canonical) shape such that the sum of its distances from each of the shapes in the group is minimized.
We will first test the approach on a set of synthetic shapes where the resulting median shape can be validated. The outcomes of this preliminary stage are expected to guide us in planning more advanced experiments.
</div>

<div style="width: 35%; float: left;">
<h3>Progress</h3>
We are about half way in the process of transforming the Matlab code into ITK.
Specifically we worked on building a new ITK boundary based metric and incorporating it into our registration framework prior to the construction
of the boundary-based median shape.

</div>

2010 Summer Project Week Shape

2010-06-25T14:01:31Z

Tammy: /* Project */

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT: Tammy Riklin Raviv
* BWH: Sylvain Bouix

==Project==
<div style="margin: 10px;">
<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Objective</h3>
Group-wise statistical analysis of brain structures is significant for clinical and neuroanatomy studies. Nevertheless, due to the inherent difficulty to properly represent the morphology of the structures of interest, the statistics is usually limited to volume and surface area measurements. Ideally, we would require an injective shape representation that is invariant rigid or even affine transformations and form a vector space. In the absence of shape representation that meets all of these requirements, it is difficult to define fundamental concepts such as the median or average shape.

We defined a novel measure for shape dissimilarity that is based on shape boundaries. This measure allows us to construct the 'median' of shapes of the same class that provides reacher morphological characteristics of the ensemble. Our goal in this project is to apply the proposed method to a population study of brain structures and test its discriminative power.
</div>

<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Approach, Plan</h3>
We use a variational framework based on level-sets to deform a representative (canonical) shape such that the sum of its distances from each of the shapes in the group is minimized.
We will first test the approach on a set of synthetic shapes where the resulting median shape can be validated. The outcomes of this preliminary stage are expected to guide us in planning more advanced experiments.
</div>

<div style="width: 35%; float: left;">
<h3>Progress</h3>
We are about half way in the process of transforming the Matlab code into ITK having successful preliminary results.

</div>

2010 Summer Project Week Breakout Session: Volume Rendering

2010-06-22T19:18:16Z

Tammy: /* Participants */

[[2010_Summer_Project_Week|Back to Project Week 2010]]

= Breakout session on Volume Rendering =
Goal of this session is to compare and contrast various volume rendering approaches in the context of clinical application scenarios. This will mainly be done through hands-on demos and group discussion.

* When: 3pm Thursday June 24
* Where: at a projector in the project week room.

Systems to examine:
* VTK GPU Raycasting in slicer 3.6 (GLSL)
* NCI GPU Raycasting in slicer 3.6 (GLSL)
* Microsoft research volume rendering demo (CUDA, closed source)
* Others?

Scenarios:
* Virtual Colonoscopy
* PET-CT
* Microscopy

== Preparation ==
Earlier in the project week we will work on getting the same datasets loaded on the various systems.

== Participants ==
Please add your name to the list if you are interested in participating in this session
# Steve Pieper, Isomics, Inc.
# Julien Finet, Kitware Inc.
# Alex Yarmarkovich, Isomics, Inc.
# Yanling Liu, SAIC-Frederick/NCI-Frederick.
# Jim Barabas, MIT Media Lab.
# Arnaud Gelas, Harvard Medical School
# Nicolas Rannou, Harvard Medical School
# Andriy Fedorov, SPL
# Curtis Lisle, KnowledgeVis
# Nicholas Herlambang, AZE, Ltd.
# Tammy Riklin Raviv, CSAIL, MIT

Microscopy Image Analysis

2010-06-21T19:26:06Z

Tammy: /* Participants */

= Open Workshop on Microscopy Image Analysis in ITK and VTK =
This workshop is part of the [[2010_Summer_Project_Week]] at MIT. The goal of this workshop is to foster the growth of a community of scientists interested in microscopy image analysis for biology using ITK and VTK

== Preparation ==
If you would like to participate in this workshop then please:
* Add your name to the "Participants" section below
* Add your project to [[2010_Summer_Project_Week#Microscopy Image Analysis|Microscopy Image Analysis]] projects list on the main page
* Register for the overall conference from [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here]
* Create a wiki page describing your project following the preparation instructions on the [[2010_Summer_Project_Week#Preparation]] home page and link this to your project listing

== Background ==
Optical microscopy is by far the most common form of imaging in biomedical research due to its high spatial resolution (subcellular), high specificity (molecular in the case of fluorescence), and suitability for use in living specimens. A Google Scholar search for "fluorescence microscopy", only one of several types of optical microscopy, returns 1.7 million articles compared with < 1 million for "MRI". Traditionally, the vast majority of these users of microscopy have performed qualitative analysis on a small number of images, but this is quickly changing. There is increasingly a need to perform quantitative analysis on microscopy images and to perform this analysis on large image sets (>100,000 images). In addition to higher throughput, recent advances in microscopy have made higher dimensional imaging commonplace. Researchers now routinely capture microscopy images over the dimensions of space (x,y,z), time (t), and multiple channels of color (lambda). Due to the large datasets, high dimensions, and complexity of analysis, current approaches to microscopy image analysis relying on Java, Matlab, and “home brew” applications are reaching their limits. We believe that a community based effort centered on developing microscopy-specific algorithms and applications built on the C++ class libraries of VTK and ITK represents the best path forward.

== Focus ==
The focus of this workshop will be on segmentation and tracking of cells in optical microscopy images. Segmentation and tracking of cells represents a very common problem in microscopy image analysis. Although there is a common pipeline for many users (e.g. image preprocessing to remove noise, detection of seeds, detection of cells at single timepoints, tracking movements over time, data analysis) the algorithm parameters and algorithms themselves are often dependent on the specifics of the experimental setup. There is thus a strong need to develop a framework to allow users to choose algorithms and tune parameters to most importantly achieve robust segmentation and secondarily minimize computational cost.

==Format==
The format for this meeting will be as a “track” within the NAMIC Project Week 2010 meeting at MIT in Boston, MA on June 21-25. Participants in this workshop should all have specific coding projects relating to cell segmentation and tracking that they wish to complete within the week. Ideally these projects should be collaborative so as to benefit from the gathering of researchers at the conference. At the beginning of the meeting on Monday, workshop participants will present a 1 slide summary of the goals of their project as part of the overall meeting. This slide will take the form of a templated wiki page. For the rest of the week, workshop participants will sit in a common area and code on their projects. We will also have a microscopy breakout session on Wednesday. These project weeks tend to be quite productive because of the concentration of available expertise at the meeting. During the week we will also break from the coding to have a more formal discussion of our current individual efforts, the needs of the microscopy community, the technical issues of combining and exchanging code, and how we should move forward.

== Schedule ==
* Monday afternoon- 1 slide lightning talk of project planned for the week using your project page
* Wednesday afternoon
** 1:00pm - 2:20pm: Current efforts (20 minute talks per lab). The goal is to describe the user application you are focussed on, your software approach (demos of software are great), and how others can interface with your efforts.
*** 1:00pm: Megason Lab- Dept of Systems Biology, Harvard
**** Sean Megason - Microscopy image analysis for into imaging of embryogenesis
**** Lydie Souhait - Demo of GoFigure
**** Arnaud Gelas - Interfacing with the Megason Lab
*** 1:20pm: Palaniappan Lab- Univ of Missouri
*** 1:40pm: Machiraju Lab- Ohio State Univ
*** 2:00pm: Roysam Lab- Rensselaer Polytechnic Institute
*** 2:20pm: Gouaillard Lab - Singapore Immunology Network / President Cosmo Software
** 2:40pm: Roundtable discussion of standards/interfaces
*** Image file types
*** Input-output interface for segmentation and tracking filters
*** Format for outputted data (e.g. automatic annotations of cell size, intensity, cell type)
*** Greatest common denominator of code: ITK classes, compound filters in ITK, plugins?
*** Common human tasks
**** Manual segmentation and editing of results
**** Visualization of results
** Future directions
* Friday- 1 slide summary of results for the week using your project page
* The rest of the time will be spent coding on projects

== Projects ==
The meat of this workshop is project work. This work should be collaborative to fully take advantage of everyone being together at the conference, to learn other people's approaches, and to flesh out the important needs of microscopy image analysis. If you need help formulating a project please contact Arnaud Gelas (arnaud_gelas@hms.harvard.edu) who can help as a matchmaker. Please list your projects in the [[2010_Summer_Project_Week#Microscopy Image Analysis|Microscopy Image Analysis Project]] section of the main page

== Participants ==
Please add your name to the list if you are interested in participating in this workshop
# Raghu Machiraju, Ohio State University
# Thierry Pecot, Ohio State University
# Shantanu Singh, Ohio State University
# Liya Ding, Ohio State University
# Kannappan Palaniappan, University of Missouri
# Ilker Ersoy, University of Missouri
# Adel Hafiane, ENSI-Bourges, France
# Yousef Al-Kofahi, Rensselaer Polytechnic Institute, CompuCyte Corporation
# Kedar Grama, Rensselaer Polytechnic Institute
# Raghav Padmanabhan, Rensselaer Polytechnic Institute
# Arnaud Gelas, Harvard Medical School
# Kishore Mosaliganti, Harvard Medical School
# Nicolas Rannou, Harvard Medical School
# Antonin Perrot-Audet, Harvard Medical School
# Lydie Souhait, Harvard Medical School
# Sean Megason, Harvard Medical School
# Luis Ibanez, Kitware
# Andinet Enquobahrie, Kitware
# Mathieu Malaterre, CoSMo
# Alex. Gouaillard. A*STAR / CoSMo
# Sonia Pujol. Brigham and Women's Hospital
# Steve Pieper, Isomics, Inc.
# Alex Yarmarkovich, Isomics, Inc.
# Curtis Lisle, KnowledgeVis
# Tammy Riklin Raviv, CSAIL, MIT

2010 Summer Project Week Shape

2010-06-07T15:22:49Z

Tammy: /* Project */

2010 Summer Project Week Shape

2010-06-07T15:22:17Z

Tammy: /* Project */

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT: Tammy Riklin Raviv
* BWH: Sylvain Bouix

==Project==
<div style="margin: 10px;">
<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Objective</h3>
Group-wise statistical analysis of brain structures is significant for clinical and neuroanatomy studies. Nevertheless, due to the inherent difficulty to properly represent the morphology of the structures of interest, the statistics is usually limited to volume and surface area measurements. Ideally, we would require an injective shape representation that is invariant rigid or even affine transformations and form a vector space. In the absence of shape representation that meets all of these requirements, it is difficult to define fundamental concepts such as the median or average shape.
We defined a novel measure for shape dissimilarity that is based on shape boundaries. This measure allows us to construct the 'median' of shapes of the same class that provides reacher morphological characteristics of the ensemble. Our goal in this project is to apply the proposed method to a population study of brain structures and test its discriminative power.
</div>

<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Approach, Plan</h3>
We use a variational framework based on level-sets to deform a representative (canonical) shape such that the sum of its distances from each of the shapes in the group is minimized.
We will first test the approach on a set of synthetic shapes where the resulting median shape can be validated. The outcomes of this preliminary stage are expected to guide us in planning more advanced experiments.
</div>

<div style="width: 35%; float: left;">
<h3>Progress</h3>

</div>

2010 Summer Project Week

2010-06-04T14:42:00Z

Tammy: /* Shape Analysis */

__NOTOC__

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

[[Image:PW-MIT2010.png|300px]]

==Background==

We are pleased to announce the 11th 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.

Active preparation begins on Thursday, April 15th 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 30-50 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 21-25, 2010
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&app=resvlink&fromDate=6/20/10&toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process.

''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''
''' $189/night/room (plus tax).'''
''' This rate is good only through June 1.'''

Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290.

*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

==Agenda==
=== Monday, June 21, 2010 ===
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template])
** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)

=== Tuesday, June 22, 2010 ===
** 8:30am breakfast
**9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.
**9:45-10:30am 3D Slicer Update (Steve Pieper)
**10:30-11am OpenIGTLink Update (Junichi Tokuda)
**11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)
** noon lunch
** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)
** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]
** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)
** 5:30pm adjourn for day

=== Wednesday, June 23, 2010 ===
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
**12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]
**1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)
**3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)
**3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)
** 5:30pm adjourn for day

=== Thursday, June 24, 2010 ===
** 8:30am breakfast

** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
** noon lunch
** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)
** 2-2:30pm: [http://www.commontk.org/index.php/Build_Instructions#Simple_Git Simple Git] (Steve Pieper)
** 5:30pm adjourn for day

=== Friday, June 25, 2010 ===
** 8:30am breakfast
** 10am-noon: [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]

==Projects==

=== Segmentation ===
*[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)
*[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)
*[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)
*[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)
*[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)
*[[2010_Summer_Project_Week_White Matter Lesion segmentation|White Matter Lesion segmentation]] (Minjeong Kim, Xiaodong Tao, Jim Miller, Dinggang Shen)
*[[2010_Summer_Project_Week_Left ventricular scar segmentation| LV scar segmentation display and fusion]] (Dana C. Peters, Felix Liu, BIDMC, Boston)

=== Registration ===
*[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)
*[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)
*[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)
*[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)
*[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)
*[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)
*[[2010_Summer_Project_Week_Groupwise_Registration|Groupwise Registration]] (Ryan Eckbo, Jim Miller, Hans Johnson)

=== IGT ===
*[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, Dominik Meier, Hans Johnson)
*Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)
* Liver Ablation (Haiying Liu)
* BrainLab-Aurora HybridNav (Isaiah Norton, Dan Marcus)
*[[2010_Summer_Project_Week_Dynamic_Image_Fusion_for_Guidance_of_Cardiac_Therapies|Dynamic Image Fusion for Guidance of Cardiac Therapies]] (Feng Li)

=== Radiotherapy ===
*[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)
*[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)

=== Analysis ===
*Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)
*Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)
*[[2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface| MRSI module and SIVIC interface]] (B Menze, M Phothilimthana, J Crane (UCSF), B Olson (UCSF), P Golland)
*[[NAMIC Tools Suite for DTI analysis]] (Hans Johnson, Joy Matsui, Vincent Magnotta, Sylvain Gouttard)
*[[Automatic SPHARM Shape Analysis in 3D Slicer ]] (Corentin Hamel, Clement Vachet, Beatriz Paniagua, Nicolas Augier, Martin Styner)

===[[Microscopy Image Analysis]] ===
* Malaterre, Gouaillard: DICOM supplement [ftp://medical.nema.org/medical/dicom/supps/sup145_09.pdf 145]: Microscopy Image in the Dicom Standard
* Laehman, Gouaillard: Microscopy pre-processing extension of ITK: convolution, deconvolution, wavelets and more
* Gouaillard: Flow Cytometry
* [[Import/Export Farsight-GoFigure results]] (Lydie Souhait, Arnaud Gelas, Sean Megason, Badri Roysam)
* [[Farsight nuclear segmentation as GoFigure plugin]] (Arnaud Gelas, Sean Megason, Badri Roysam)
* [[ITK Spherical Harmonics filter for shape analysis of cell nuclei]] (Shantanu Singh, Arnaud Gelas, Sean Megason, Raghu Machiraju)
* [[CTK Transfer function widget]] (Nicolas Rannou, Julien Finet, Stever Pieper)
* [[Seedings results comparison]] (Antonin Perrot-Audet, Kishore Mosaliganti, Sean Megason, Badri Roysam)
* [[ITK GPAC level set]] (K. Palaniappan, Kishore Mosaliganti, Sean Megason)

=== Shape Analysis ===
*[[2010_Summer_Project_Week_Shape|Median Shape by Boundary-based Distance ]](Tammy Riklin Raviv, Sylvain Bouix)

=== Informatics ===
* Computer Aided Photodynamic Therapy (Pietka, Spinczyk)

=== Diffusion ===
*Fluid Mechanics Based Tractography (Nathan Hageman)
*[[Efficient Diffusion Connectivity via Multidirectional Fstar]] (Alexis Boucharin, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner)

=== NA-MIC Kit Internals ===
*Module Inventory (Steve, Jim)
*Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)
* [[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)
*XNAT Enterprise webservices client for Slicer (Wendy, Mark)
*[[2010_Summer_Project_Week_PythonQt|PythonQt and console widget]] (Steve Pieper, Jean-Christophe Fillion-Robin)

*VTKWidgets (JC, will, Schroeder, Nicole, Ron)
*Superbuild (Dave Partika, Steve Pieper, Katie Hayes)
*[[Paraview Support for Computational Anatomy]] (Michel Audette, Mike Bowers)

== 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 15, 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 June 10, 2009: [[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 17, 2010: 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].

==Attendee List==

<big>'''NOTE:'''</big> <font color="maroon">THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]</font>

#Aucoin, Nicole, BWH
#Audette, Michel, Kitware
#Aylward, Stephen, Kitware, Inc
#Boucharin, Alexis, UNC Neuro Image Research and Analysis Laboratories
#Bouix, Sylvain, BWH
#Budin, Francois, UNC
#Burdette, Everette, Acoustic MedSystems, Inc.
#Chen, Min, Johns Hopkins University
#Datar, Manasi, SCI Institute
#Eckbo, Ryan, BWH
#Fedorov, Andriy, Surgical Planning Lab
#Fillion-Robin, Jean-Christophe, Kitware Inc.
#Finet, Julien, Kitware Inc
#Fishbaugh, James, SCI Institute
#Gao, Yi, Gerogia Tech
#GELAS, Arnaud, Harvard Medical School
#gouaillard, alexandre, CoSMo Software
#Gouttard, Sylvain, SCI Institute
#Haehn, Daniel, University of Pennsylvania
#Hageman, Nathan
#Hahn, Dieter, University Erlangen
#Hamel, Corentin, UNC Chapel Hill
#Hata, Nobuhiko, Brigham and Women's Hospital
#Hayes, Kathryn, Brigham and Women's Hospital
#Holton, Leslie, Medtronic Navigation
#Ibanez, Luis, KITWARE Inc.
#Johnson, Hans, University of Iowa
#Kapur, Tina, Brigham and Women's Hospital
#Kikinis, Ron, Brigham and Women's Hospital
#Kim, Minjeong, UNC-Chapel Hill
#Kolesov, Ivan, Georgia Institute of Technology
#Larson, Garrett, UNC-CH
#Li, Rui, MGH
#Lisle, Curtis, KnowledgeVis, LLC
#Liu, Haiying, Brigham and Women's Hospital
#Liu, Yanling, SAIC-Frederick, Inc.
#Magnotta, Vincent, The University of Iowa
#malaterre, mathieu, CoSMo Software
#Mastrogiacomo, Katie, Brigham and Women's Hospital
#Matsui, Joy, University
#Megason, Sean, Harvard Medical School
#Meier, Dominik, BWH, Boston MA
#menze, bjoern, CSAIL MIT
#Mosaliganti, Kishore, Harvard Medical School
#Niethammer, Marc, UNC Chapel Hill
#Norton, Isaiah, BWH Neurosurgery
#Paniagua, Beatriz, University of North Caolina at Chapel Hill
#Papademetris, Xenophon, Yale University
#Partyka, David, Kitware Inc
#Pathak, Sudhir, Univeristy Of Pittsburgh
#Peroni, Marta, Politecnico di Milano
#Perrot-Audet, Antonin, Harvard Medical School
#Pieper, Steve, Isomics, Inc.
#Plesniak, Wendy, BWH
#Pohl, Kilian, IBM
#Pujol, Sonia, Brigham and Women's Hospital
#Rannou, Nicolas, Harvard Medical School
#Riklin Raviv, Tammy, MIT, CSAIL
#Ruiz, Marco, UCSD
#Schroeder, William, Kitware
#Scully, Mark, The Mind Research Network
#Sharp, Greg, MGH
#Shi, Yundi, UNC Chapel Hill
#Shusharina, Nadya, MGH
#Smith, Gareth, Wolfson Medical Imaging Centre (WMIC)
#Souhait, Lydie, Harvard Medical School
#Spinczyk, Dominik, Silesian University of Technology
#Srinivasan, Padmapriya
#Tao, Xiaodong, GE Research
#Ungi, Tamas, Queen's University
#Vachet, Clement, UNC Chapel Hill
#Veni, Gopalkrishna, SCI Institute
#Wassermann, Demian, SPL/LMI/PNL
#Wells, Sandy, BWH
#Wu, Guorong, University of North Carolina at Chapel Hill

2010 Summer Project Week

2010-06-04T14:39:25Z

Tammy: /* Shape Analysis */

__NOTOC__

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

[[Image:PW-MIT2010.png|300px]]

==Background==

We are pleased to announce the 11th 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.

Active preparation begins on Thursday, April 15th 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 30-50 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 21-25, 2010
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&app=resvlink&fromDate=6/20/10&toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process.

''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''
''' $189/night/room (plus tax).'''
''' This rate is good only through June 1.'''

Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290.

*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

==Agenda==
=== Monday, June 21, 2010 ===
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template])
** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)

=== Tuesday, June 22, 2010 ===
** 8:30am breakfast
**9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.
**9:45-10:30am 3D Slicer Update (Steve Pieper)
**10:30-11am OpenIGTLink Update (Junichi Tokuda)
**11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)
** noon lunch
** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)
** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]
** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)
** 5:30pm adjourn for day

=== Wednesday, June 23, 2010 ===
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
**12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]
**1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)
**3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)
**3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)
** 5:30pm adjourn for day

=== Thursday, June 24, 2010 ===
** 8:30am breakfast

** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
** noon lunch
** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)
** 2-2:30pm: [http://www.commontk.org/index.php/Build_Instructions#Simple_Git Simple Git] (Steve Pieper)
** 5:30pm adjourn for day

=== Friday, June 25, 2010 ===
** 8:30am breakfast
** 10am-noon: [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]

==Projects==

=== Segmentation ===
*[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)
*[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)
*[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)
*[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)
*[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)
*[[2010_Summer_Project_Week_White Matter Lesion segmentation|White Matter Lesion segmentation]] (Minjeong Kim, Xiaodong Tao, Jim Miller, Dinggang Shen)
*[[2010_Summer_Project_Week_Left ventricular scar segmentation| LV scar segmentation display and fusion]] (Dana C. Peters, Felix Liu, BIDMC, Boston)

=== Registration ===
*[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)
*[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)
*[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)
*[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)
*[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)
*[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)
*[[2010_Summer_Project_Week_Groupwise_Registration|Groupwise Registration]] (Ryan Eckbo, Jim Miller, Hans Johnson)

=== IGT ===
*[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, Dominik Meier, Hans Johnson)
*Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)
* Liver Ablation (Haiying Liu)
* BrainLab-Aurora HybridNav (Isaiah Norton, Dan Marcus)
*[[2010_Summer_Project_Week_Dynamic_Image_Fusion_for_Guidance_of_Cardiac_Therapies|Dynamic Image Fusion for Guidance of Cardiac Therapies]] (Feng Li)

=== Radiotherapy ===
*[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)
*[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)

=== Analysis ===
*Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)
*Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)
*[[2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface| MRSI module and SIVIC interface]] (B Menze, M Phothilimthana, J Crane (UCSF), B Olson (UCSF), P Golland)
*[[NAMIC Tools Suite for DTI analysis]] (Hans Johnson, Joy Matsui, Vincent Magnotta, Sylvain Gouttard)
*[[Automatic SPHARM Shape Analysis in 3D Slicer ]] (Corentin Hamel, Clement Vachet, Beatriz Paniagua, Nicolas Augier, Martin Styner)

===[[Microscopy Image Analysis]] ===
* Malaterre, Gouaillard: DICOM supplement [ftp://medical.nema.org/medical/dicom/supps/sup145_09.pdf 145]: Microscopy Image in the Dicom Standard
* Laehman, Gouaillard: Microscopy pre-processing extension of ITK: convolution, deconvolution, wavelets and more
* Gouaillard: Flow Cytometry
* [[Import/Export Farsight-GoFigure results]] (Lydie Souhait, Arnaud Gelas, Sean Megason, Badri Roysam)
* [[Farsight nuclear segmentation as GoFigure plugin]] (Arnaud Gelas, Sean Megason, Badri Roysam)
* [[ITK Spherical Harmonics filter for shape analysis of cell nuclei]] (Shantanu Singh, Arnaud Gelas, Sean Megason, Raghu Machiraju)
* [[CTK Transfer function widget]] (Nicolas Rannou, Julien Finet, Stever Pieper)
* [[Seedings results comparison]] (Antonin Perrot-Audet, Kishore Mosaliganti, Sean Megason, Badri Roysam)
* [[ITK GPAC level set]] (K. Palaniappan, Kishore Mosaliganti, Sean Megason)

=== Shape Analysis ===
*[[2010_Summer_Project_Week:_Shape|Median Shape by Boundary-based Distance ]](Tammy Riklin Raviv, Sylvain Bouix)

=== Informatics ===
* Computer Aided Photodynamic Therapy (Pietka, Spinczyk)

=== Diffusion ===
*Fluid Mechanics Based Tractography (Nathan Hageman)
*[[Efficient Diffusion Connectivity via Multidirectional Fstar]] (Alexis Boucharin, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner)

=== NA-MIC Kit Internals ===
*Module Inventory (Steve, Jim)
*Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)
* [[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)
*XNAT Enterprise webservices client for Slicer (Wendy, Mark)
*[[2010_Summer_Project_Week_PythonQt|PythonQt and console widget]] (Steve Pieper, Jean-Christophe Fillion-Robin)

*VTKWidgets (JC, will, Schroeder, Nicole, Ron)
*Superbuild (Dave Partika, Steve Pieper, Katie Hayes)
*[[Paraview Support for Computational Anatomy]] (Michel Audette, Mike Bowers)

== 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 15, 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 June 10, 2009: [[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 17, 2010: 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].

==Attendee List==

<big>'''NOTE:'''</big> <font color="maroon">THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]</font>

#Aucoin, Nicole, BWH
#Audette, Michel, Kitware
#Aylward, Stephen, Kitware, Inc
#Boucharin, Alexis, UNC Neuro Image Research and Analysis Laboratories
#Bouix, Sylvain, BWH
#Budin, Francois, UNC
#Burdette, Everette, Acoustic MedSystems, Inc.
#Chen, Min, Johns Hopkins University
#Datar, Manasi, SCI Institute
#Eckbo, Ryan, BWH
#Fedorov, Andriy, Surgical Planning Lab
#Fillion-Robin, Jean-Christophe, Kitware Inc.
#Finet, Julien, Kitware Inc
#Fishbaugh, James, SCI Institute
#Gao, Yi, Gerogia Tech
#GELAS, Arnaud, Harvard Medical School
#gouaillard, alexandre, CoSMo Software
#Gouttard, Sylvain, SCI Institute
#Haehn, Daniel, University of Pennsylvania
#Hageman, Nathan
#Hahn, Dieter, University Erlangen
#Hamel, Corentin, UNC Chapel Hill
#Hata, Nobuhiko, Brigham and Women's Hospital
#Hayes, Kathryn, Brigham and Women's Hospital
#Holton, Leslie, Medtronic Navigation
#Ibanez, Luis, KITWARE Inc.
#Johnson, Hans, University of Iowa
#Kapur, Tina, Brigham and Women's Hospital
#Kikinis, Ron, Brigham and Women's Hospital
#Kim, Minjeong, UNC-Chapel Hill
#Kolesov, Ivan, Georgia Institute of Technology
#Larson, Garrett, UNC-CH
#Li, Rui, MGH
#Lisle, Curtis, KnowledgeVis, LLC
#Liu, Haiying, Brigham and Women's Hospital
#Liu, Yanling, SAIC-Frederick, Inc.
#Magnotta, Vincent, The University of Iowa
#malaterre, mathieu, CoSMo Software
#Mastrogiacomo, Katie, Brigham and Women's Hospital
#Matsui, Joy, University
#Megason, Sean, Harvard Medical School
#Meier, Dominik, BWH, Boston MA
#menze, bjoern, CSAIL MIT
#Mosaliganti, Kishore, Harvard Medical School
#Niethammer, Marc, UNC Chapel Hill
#Norton, Isaiah, BWH Neurosurgery
#Paniagua, Beatriz, University of North Caolina at Chapel Hill
#Papademetris, Xenophon, Yale University
#Partyka, David, Kitware Inc
#Pathak, Sudhir, Univeristy Of Pittsburgh
#Peroni, Marta, Politecnico di Milano
#Perrot-Audet, Antonin, Harvard Medical School
#Pieper, Steve, Isomics, Inc.
#Plesniak, Wendy, BWH
#Pohl, Kilian, IBM
#Pujol, Sonia, Brigham and Women's Hospital
#Rannou, Nicolas, Harvard Medical School
#Riklin Raviv, Tammy, MIT, CSAIL
#Ruiz, Marco, UCSD
#Schroeder, William, Kitware
#Scully, Mark, The Mind Research Network
#Sharp, Greg, MGH
#Shi, Yundi, UNC Chapel Hill
#Shusharina, Nadya, MGH
#Smith, Gareth, Wolfson Medical Imaging Centre (WMIC)
#Souhait, Lydie, Harvard Medical School
#Spinczyk, Dominik, Silesian University of Technology
#Srinivasan, Padmapriya
#Tao, Xiaodong, GE Research
#Ungi, Tamas, Queen's University
#Vachet, Clement, UNC Chapel Hill
#Veni, Gopalkrishna, SCI Institute
#Wassermann, Demian, SPL/LMI/PNL
#Wells, Sandy, BWH
#Wu, Guorong, University of North Carolina at Chapel Hill

2010 Summer Project Week Shape

2010-06-04T14:37:35Z

Tammy: Created page with '__NOTOC__ <gallery> Image:PW-MIT2010.png|Projects List </gallery> ==Key Investigators== * MIT: Tammy Riklin Raviv * BWH: Sylvain Bouix ==…'

__NOTOC__
<gallery>
Image:PW-MIT2010.png|[[2010_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* MIT: Tammy Riklin Raviv
* BWH: Sylvain Bouix

==Project==
<div style="margin: 10px;">
<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Objective</h3>
Group-wise statistical analysis of brain structures is significant for clinical and neuroanatomy studies. Nevertheless, due the inherent difficulty to properly represent the morphology of the structures of interest, the statistics is usually limited to volume and surface area measurements. Ideally, we would require an injective shape representation that is invariant rigid or even affine transformations and form a vector space. In the absence of shape representation that meets all of these requirements, it is difficult to define fundamental concepts such as the median or average shape.
We defined a novel measure for shape dissimilarity that is based on shape boundaries. This measure allows us to construct the 'median' of shapes of the same class that provides reacher morphological characteristics of the ensemble. Our goal in this project is to apply the proposed method to a population study of brain structures and test its discriminative power.
</div>

<div style="width: 30%; float: left; padding-right: 2%;">
<h3>Approach, Plan</h3>
We use a variational framework based on level-sets to deform a representative (canonical) shape such that the sum of its distances from each of the shapes in the group is minimized.
We will first test the approach on a set of synthetic shapes where the resulting median shape can be validated. The outcomes of this preliminary stage are expected to guide us in planning more advanced experiments.
</div>

<div style="width: 35%; float: left;">
<h3>Progress</h3>

</div>

2010 Summer Project Week

2010-06-04T13:43:39Z

Tammy: /* Shape Analysis */

2010 Summer Project Week

2010-06-03T18:57:31Z

Tammy: /* Projects */

__NOTOC__

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

[[Image:PW-MIT2010.png|300px]]

==Background==

We are pleased to announce the 11th 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.

Active preparation begins on Thursday, April 15th 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 30-50 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 21-25, 2010
*'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].
*'''REGISTRATION:''' Please click [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 here] to do an on-line registration for the meeting that will allow you to pay by credit card, or send a check.
*'''Registration Fee:''' $260 (covers the cost of breakfast, lunch and coffee breaks for the week).
*'''Hotel:''' We have reserved a block of rooms at the Boston Marriott Cambridge Hotel, Two Cambridge Center, 50 Broadway, Cambridge, MA 02142. (Phone: 617.252.4405, Fax: 617.494.6565) [http://www.marriott.com/hotels/travel/BOSCB?groupCode=NAMNAMA&app=resvlink&fromDate=6/20/10&toDate=6/25/10 Please click here to reserve.] You will be directed to the property's home page with the group code already entered in the appropriate field. All you need to do is enter your arrival date to begin the reservation process.

''' All reservations must be made by Tuesday, June 1, 2010 to receive the discounted rate of'''
''' $189/night/room (plus tax).'''
''' This rate is good only through June 1.'''

Please note that if you try to reserve a room outside of the block on the shoulder nights via the link, you will be told that the group rate is not available for the duration of your stay. To reserve those rooms, which might not be at the group rate because it is based upon availability, please call Marriott Central Reservations at 1-800-228-9290.

*Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.
*For hosting projects, we are planning to make use of the NITRC resources. See [[NA-MIC_and_NITRC | Information about NITRC Collaboration]]

==Agenda==
=== Monday, June 21, 2010 ===
** noon-1pm lunch
**1pm: Welcome (Ron Kikinis)
** 1:05-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template Wiki Template])
** 3:30-5:30pm Tutorial: [[2010 Summer Project Week Breakout: Getting Started with Qt]] (Adam Weinrich, Nokia)

=== Tuesday, June 22, 2010 ===
** 8:30am breakfast
**9-9:45am: NA-MIC Kit Update (Jim Miller) - include Module nomenclature (Extensions: cmdline vs loadable, Built-in), QT, Include Superbuild demo by Dave P.
**9:45-10:30am 3D Slicer Update (Steve Pieper)
**10:30-11am OpenIGTLink Update (Junichi Tokuda)
**11-12pm: Slicer Hands-on Workshop (Randy Gollub, Sonia Pujol)
** noon lunch
** 1-3pm: Breakout Session: QT/Slicer (Steve, JC, J2) (w/ possible QnA with QT experts)
** 3pm: [[Summer_2010_Tutorial_Contest|Tutorial Contest Presentations]]
** 4-5pm [[2010 Summer Project Week Breakout Session: Data Management]] (Dan Marcus, Stephen Aylward)
** 5:30pm adjourn for day

=== Wednesday, June 23, 2010 ===
** 8:30am breakfast
** 9am-12pm Breakout Session: [[2010 Project Week Breakout Session: ITK]] (Luis Ibanez)
** noon lunch
**12:45pm: [[Events:TutorialContestJune2010|Tutorial Contest Winner Announcement]]
**1-3pm: Breakout Session: [[Microscopy_Image_Analysis]] (Sean Megason)
**3-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:QA Training]] (Luis Ibanesz)
**3-4pm: Breakout Session: [[2010 Summer Project Week Breakout Session:VTK Widget]] (Nicole, Kilian, JC)
** 5:30pm adjourn for day

=== Thursday, June 24, 2010 ===
** 8:30am breakfast
** 9am-5pm: Breakout Session: [[2010 Summer Project Week Breakout Session:OpenIGTLink|OpenIGTLink]]
** noon lunch
** 1-2pm: [[2010 Summer Project Week Breakout Session:GWE]] (Marco Ruiz)
** 5:30pm adjourn for day

=== Friday, June 25, 2010 ===
** 8:30am breakfast
** 10am-noon: [[#Projects|Project Progress Updates]]
*** Noon: Lunch boxes and adjourn by 1:30pm.
***We need to empty room by 1:30. You are welcome to use wireless in Stata.
***Please sign up for the developer [http://www.slicer.org/pages/Mailinglist mailing lists]
***Next Project Week [[AHM_2011|in Utah, Fill in Dates]]

==Projects==

=== Segmentation ===
*[[2010_Summer_Project_Week_Robust_Statistics_Segmenter_Slicer_Module|Robust Statistics Segmenter Slicer Module]] (Yi Gao, Allen Tannenbaum, Ron Kikinis)
*[[2010_Summer_Project_Week_Multi_scale_Shape_Based_Segmentation_for_the_Hippocampus|Multi-scale Shape Based Segmentation for the Hippocampus]] (Yi Gao, Allen Tannenbaum)
*[[2010_Summer_Project_Week/The Vascular Modeling Toolkit in 3D Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Kilian Pohl, Steve Pieper, Ron Kikinis)
*[[2010_Summer_Project_Week_Prostate_MRI_Segmentation|Prostate Segmentation from MRI]] (Andriy Fedorov, Yi Gao)
*[[2010_Summer_Project_Week_SPECTRE|SPECTRE: Skull Stripping integration with Slicer]] (Nicole Aucoin, Min Chen)
*[[2010_Summer_Project_Week_White Matter Lesion segmentation|White Matter Lesion segmentation]] (Minjeong Kim, Xiaodong Tao, Jim Miller, Dinggang Shen)

=== Registration ===
*[[2010_Summer_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library]] (Dominik Meier)
*[[2010_Summer_Project_Week_Fiducial_Deformable_Registration|Fiducial-based deformable image registration]] (Nadya Shusharina, Greg Sharp)
*[[2010_Summer_Project_Week_HAMMER: Deformable Registration|HAMMER: Deformable Registration]] (Guorong Wu, Xiaodong Tao, Jim Miller, Dinggang Shen)
*[[2010_Summer_Project_Week_Best_Regularization_Term_for_Demons_Registration_Algorithm|Best Regularization Term for Demons Registration Algorithm]] (Rui Li, Greg Sharp)
*[[2010_Summer_Project_Week_RegistrationEvaluation|Evaluation of Registration in Slicer]] (James Fishbaugh, Guido Gerig, Domink Meier)
*[[2010_Summer_Project_Week_MR_to_CT_Registration_for_Prostate_Brachytherapy_Planning|MR to CT Registration for Prostate Brachytherapy Planning]] (Andriy Fedorov, ?)
*[[2010_Summer_Project_Week_MR_to_Ultrasound_Registration_Methodology|MR to Ultrasound Registration Methodology]] (Dieter Hahn, William Wells, Joachim Hornegger, Tina Kapur, Stephen Aylward)
*[[2010_Summer_Project_Week_Groupwise_Registration|Groupwise Registration]] (Ryan Eckbo)

=== IGT ===
*Prostate Intervention(Junichi, Sam Song, Tamas Ungi?)
* Liver Ablation (Haiying Liu)
* BrainLab-Aurora HybridNav (Isaiah Norton)

=== Radiotherapy ===
*[[2010_Summer_Project_Week_DICOM_RT|Dicom RT plugin]] (Greg Sharp)
*[[2010_Summer_Project_Week_HandN_Cancer|Adaptive Radiation Therapy for H&N cancer]] (Marta Peroni,Polina Golland,Greg Sharp)

=== Analysis ===
*Femoral Fracture Classification Brainstorming Session (Karl F, Vince M, Peter Karasev, Curt Lisle, Ron)
*Cortical thickness analysis (Clement Vachet, Heather Cody Hazlett, Martin Styner)
*[[2010_Summer_Project_Week_MRSI_module_and_SIVIC_interface| MRSI module and SIVIC interface]] (B Menze, M Phothilimthana, J Crane (UCSF), B Olson (UCSF), P Golland)
*[[NAMIC Tools Suite for DTI analysis]] (Hans Johnson, Joy Matsui, Vincent Magnotta, Sylvain Gouttard)
*[[Automatic SPHARM Shape Analysis in 3D Slicer ]] (Corentin Hamel, Clement Vachet, Beatriz Paniagua, Nicolas Augier, Martin Styner)

===[[Microscopy Image Analysis]] ===
* Malaterre, Gouaillard: DICOM supplement [ftp://medical.nema.org/medical/dicom/supps/sup145_09.pdf 145]: Microscopy Image in the Dicom Standard
* Laehman, Gouaillard: Microscopy pre-processing extension of ITK: convolution, deconvolution, wavelets and more
* Gouaillard: Flow Cytometry
* Arnaud Gelas, Sean Megason, Badri Roysam: Wrapping FARSIGHT nuclear segmentation algorithm as a GoFigure plugin.
* Shantanu Singh, Arnaud Gelas, Sean Megason, Raghu Machiraju: ITK Spherical Harmonics filter for shape analysis of cell nuclei

=== Shape Analysis ===
* Median Shape by Boundary-based Distance (Tammy Riklin Raviv, Sylvain Bouix)
=== Informatics ===
* Computer Aided Photodynamic Therapy (Pietka, Spinczyk)

=== Diffusion ===
*Fluid Mechanics Based Tractography (Nathan Hageman)
*[[Efficient Diffusion Connectivity via Multidirectional Fstar]] (Alexis Boucharin, Clement Vachet, Yundi Shi, Mar Sanchez, Martin Styner)

=== Python ===
*[[2010_Summer_Project_Week_PythonQt|PythonQt and console widget]] (Steve Pieper, Jean-Christophe Fillion-Robin)

=== Slicer Internals ===
*Module Inventory (Steve, Jim)
*Viewer Manager Factory (Alex Y., Kilian, Steve, Nicole)
* [[2010 NAMIC Project week: Programmatic use of Volume Rendering module|Programmatic use of Volume Rendering module]] (Andrey Fedorov, Yanling Liu, Alex Yarmarkovich)

=== Execution Model ===

===Other NA-MIC Kit Internals===
*VTKWidgets (JC, will, Schroeder, Nicole, Ron)
*Superbuild (Dave Partika, Steve Pieper, Katie Hayes)
*[[Paraview Support for Computational Anatomy]] (Michel Audette, Mike Bowers)

== 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 15, 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 June 10, 2009: [[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 17, 2010: 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].

==Attendee List==

<big>'''NOTE:'''</big> <font color="maroon">THIS IS AN AUTOMATICALLY GENERATED LIST FROM THE REGISTRATION WEBSITE. ATTENDEES SHOULD '''NOT''' EDIT THIS, BUT [http://guest.cvent.com/i.aspx?4W%2cM3%2c8e73686a-1432-40f2-bc78-f9e18d8bce00 REGISTER BY CLICKING HERE.]</font>

#Aucoin, Nicole, BWH
#Audette, Michel, Kitware
#Aylward, Stephen, Kitware, Inc
#Boucharin, Alexis, UNC Neuro Image Research and Analysis Laboratories
#Bouix, Sylvain, BWH
#Budin, Francois, UNC
#Burdette, Everette, Acoustic MedSystems, Inc.
#Chen, Min, Johns Hopkins University
#Datar, Manasi, SCI Institute
#Eckbo, Ryan, BWH
#Fedorov, Andriy, Surgical Planning Lab
#Fillion-Robin, Jean-Christophe, Kitware Inc.
#Finet, Julien, Kitware Inc
#Fishbaugh, James, SCI Institute
#Gao, Yi, Gerogia Tech
#GELAS, Arnaud, Harvard Medical School
#gouaillard, alexandre, CoSMo Software
#Gouttard, Sylvain, SCI Institute
#Haehn, Daniel, University of Pennsylvania
#Hageman, Nathan
#Hahn, Dieter, University Erlangen
#Hamel, Corentin, UNC Chapel Hill
#Hata, Nobuhiko, Brigham and Women's Hospital
#Hayes, Kathryn, Brigham and Women's Hospital
#Holton, Leslie, Medtronic Navigation
#Ibanez, Luis, KITWARE Inc.
#Johnson, Hans, University of Iowa
#Kapur, Tina, Brigham and Women's Hospital
#Kikinis, Ron, Brigham and Women's Hospital
#Kim, Minjeong, UNC-Chapel Hill
#Kolesov, Ivan, Georgia Institute of Technology
#Larson, Garrett, UNC-CH
#Li, Rui, MGH
#Lisle, Curtis, KnowledgeVis, LLC
#Liu, Haiying, Brigham and Women's Hospital
#Liu, Yanling, SAIC-Frederick, Inc.
#Magnotta, Vincent, The University of Iowa
#malaterre, mathieu, CoSMo Software
#Mastrogiacomo, Katie, Brigham and Women's Hospital
#Matsui, Joy, University
#Megason, Sean, Harvard Medical School
#Meier, Dominik, BWH, Boston MA
#menze, bjoern, CSAIL MIT
#Mosaliganti, Kishore, Harvard Medical School
#Niethammer, Marc, UNC Chapel Hill
#Norton, Isaiah, BWH Neurosurgery
#Paniagua, Beatriz, University of North Caolina at Chapel Hill
#Papademetris, Xenophon, Yale University
#Partyka, David, Kitware Inc
#Pathak, Sudhir, Univeristy Of Pittsburgh
#Peroni, Marta, Politecnico di Milano
#Perrot-Audet, Antonin, Harvard Medical School
#Pieper, Steve, Isomics, Inc.
#Plesniak, Wendy, BWH
#Pohl, Kilian, IBM
#Pujol, Sonia, Brigham and Women's Hospital
#Rannou, Nicolas, Harvard Medical School
#Riklin Raviv, Tammy, MIT, CSAIL
#Ruiz, Marco, UCSD
#Schroeder, William, Kitware
#Scully, Mark, The Mind Research Network
#Sharp, Greg, MGH
#Shi, Yundi, UNC Chapel Hill
#Shusharina, Nadya, MGH
#Smith, Gareth, Wolfson Medical Imaging Centre (WMIC)
#Souhait, Lydie, Harvard Medical School
#Spinczyk, Dominik, Silesian University of Technology
#Srinivasan, Padmapriya
#Tao, Xiaodong, GE Research
#Ungi, Tamas, Queen's University
#Vachet, Clement, UNC Chapel Hill
#Veni, Gopalkrishna, SCI Institute
#Wassermann, Demian, SPL/LMI/PNL
#Wells, Sandy, BWH
#Wu, Guorong, University of North Carolina at Chapel Hill

Projects:LatentAtlasSegmentation

2009-09-10T19:30:00Z

Tammy: /* Brain Tumor Modeling */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

== Brain Tumor Modeling ==
We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:TumorModeling| brain tumor modeling]] page.

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators =
* MIT, CSAIL [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Polina Golland
* Harvard Medical School, MGH | Koen Van Leemput
* Harvard Medical School, BWH | William M. Wells, Ron Kikinis, Martha Shenton, Sylvain Bouix

Projects:LatentAtlasSegmentation

2009-09-10T19:29:15Z

Tammy: /* Publications */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

== Brain Tumor Modeling ==
We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:BrainTumorModeling| brain tumor modeling]] page.

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators =
* MIT, CSAIL [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Polina Golland
* Harvard Medical School, MGH | Koen Van Leemput
* Harvard Medical School, BWH | William M. Wells, Ron Kikinis, Martha Shenton, Sylvain Bouix

Projects:LatentAtlasSegmentation

2009-09-10T19:25:15Z

Tammy: /* Key Investigators */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

== Brain Tumor Modeling ==
We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:BrainTumorModeling| brain tumor modeling]] page.

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators
* MIT, CSAIL [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Polina Golland
* Harvard Medical School, MGH | Koen Van Leemput
* Harvard Medical School, BWH | William M. Wells, Ron Kikinis, Martha Shenton, Sylvain Bouix

Projects:LatentAtlasSegmentation

2009-09-10T19:19:21Z

Tammy: /* Key Investigators */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

== Brain Tumor Modeling ==
We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:BrainTumorModeling| brain tumor modeling]] page.

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators =

* MIT [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], MGH, MIT | Koen Van Leemput, BWH, MIT | William M. Wells III, MIT | Polina Golland

Projects:LatentAtlasSegmentation

2009-09-10T19:12:54Z

Tammy: /* Brain Tumor Modeling */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

== Brain Tumor Modeling ==
We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to the [[Projects:BrainTumorModeling| brain tumor modeling]] page.

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators =

* MIT [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Koen Van Leemput, William M. Wells III, Polina Golland

Projects:LatentAtlasSegmentation

2009-09-10T19:10:00Z

Tammy:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

== Brain Tumor Modeling ==
We have applied the proposed algorithm to a longitudinal multi-modal Patient specific brain scans for brain tumor segmentation and modeling. In this particular application the inferred spatial parameters estimate the patient's latent anatomy. No prior information is assumed but a couple of mouse clicks that define a sphere that initializes the segmentation of the first time point. To learn more please refer to

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators =

* MIT [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Koen Van Leemput, William M. Wells III, Polina Golland

Projects:LatentAtlasSegmentation

2009-09-10T18:56:01Z

Tammy: /* Publications */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

= Publications =
''In Press''

* T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, MICCAI 2009.

* T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
Joint Segmentation using Patient specific Latent Anatomy Model, MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

= Key Investigators =

* MIT [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Koen Van Leemput, William M. Wells III, Polina Golland

Algorithm:MIT

2009-09-10T18:53:27Z

Tammy: /* Joint Segmentation of Image Ensembles via Latent Atlases */

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> Supervised Nonparametric Image Parcellation, M.R. Sabuncu, B.T. Thomas Yeo, K. Van Leemput, B. Fischl, and P. Golland. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.

<font color="red">'''New: '''</font> Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu,
B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. To be presented at PMMIA Workshop at MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|250px]]
| style="width:85%" |

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

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> Poynton C., Jenkinson M., Whalen S., Golby A.J., Wells III W. Fieldmap-Free Retrospective Registration and Distortion Correction for EPI-Based Functiona Imaging. MICCAI 2008.

|-

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[Image:TGIt.gif| 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...]]

<font color="red">'''New: '''</font> T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Joint Segmentation of Image Ensembles via Latent Atlases, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

<font color="red">'''New: '''</font>
T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland, Joint Segmentation using Patient specific Latent Anatomy Model
MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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: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

2009-09-10T18:52:14Z

Tammy: /* Joint Segmentation of Image Ensembles via Latent Atlases */

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> Supervised Nonparametric Image Parcellation, M.R. Sabuncu, B.T. Thomas Yeo, K. Van Leemput, B. Fischl, and P. Golland. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.

<font color="red">'''New: '''</font> Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu,
B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. To be presented at PMMIA Workshop at MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|250px]]
| style="width:85%" |

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

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> Poynton C., Jenkinson M., Whalen S., Golby A.J., Wells III W. Fieldmap-Free Retrospective Registration and Distortion Correction for EPI-Based Functiona Imaging. MICCAI 2008.

|-

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[Image:TGIt.gif| 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, W.M. Wells III and P. Golland,
<font color="red">'''New: '''</font> Joint Segmentation of Image Ensembles via Latent Atlases, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

<font color="red">'''New: '''</font>
T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland, Joint Segmentation using Patient specific Latent Anatomy Mode
MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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: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

2009-09-10T18:51:16Z

Tammy: /* Joint Segmentation of Image Ensembles via Latent Atlases */

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> Supervised Nonparametric Image Parcellation, M.R. Sabuncu, B.T. Thomas Yeo, K. Van Leemput, B. Fischl, and P. Golland. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.

<font color="red">'''New: '''</font> Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu,
B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. To be presented at PMMIA Workshop at MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|250px]]
| style="width:85%" |

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

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> Poynton C., Jenkinson M., Whalen S., Golby A.J., Wells III W. Fieldmap-Free Retrospective Registration and Distortion Correction for EPI-Based Functiona Imaging. MICCAI 2008.

|-

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[Image:TGIt.gif| 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, W.M. Wells III and P. Golland,
<font color="red">'''New: '''</font> Joint Segmentation of Image Ensembles via Latent Atlases, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

<font color="red">'''New: '''</font>
T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland, Joint Segmentation using Patient specific Latent Anatomy Mode
MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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: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

2009-09-10T18:49:41Z

Tammy: /* Joint Segmentation of Image Ensembles via Latent Atlases */

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> Supervised Nonparametric Image Parcellation, M.R. Sabuncu, B.T. Thomas Yeo, K. Van Leemput, B. Fischl, and P. Golland. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009.

<font color="red">'''New: '''</font> Nonparametric Mixture Models for Supervised Image Parcellation, M.R. Sabuncu,
B.T.T. Yeo, K. Van Leemput, B. Fischl, and P. Golland. To be presented at PMMIA Workshop at MICCAI 2009.

|-

{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|250px]]
| style="width:85%" |

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

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> Poynton C., Jenkinson M., Whalen S., Golby A.J., Wells III W. Fieldmap-Free Retrospective Registration and Distortion Correction for EPI-Based Functiona Imaging. MICCAI 2008.

|-

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[Image:TGIt.gif| 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...]]

<font color="red">'''New: '''</font> Joint Segmentation of Image Ensembles via Latent Atlases T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

<font color="red">'''New: '''</font> Joint Segmentation using Patient specific Latent Anatomy Mode
T. Riklin Raviv, B.H. Menze, K. Van Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W. M. Wells III and P. Golland
MICCAI workshop for Probabilistic Models on Medical Image Analysis (PMMIA) 2009

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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: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

2009-09-10T18:33:06Z

Tammy: /* Joint Segmentation of Image Ensembles via Latent Atlases */

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 =

== [[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...]]

{| cellpadding="10" style="text-align:left;"
| style="width:15%" | [[Image:MITHippocampalSubfieldSegmentation.png|250px]]
| style="width:85%" |

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

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> Poynton C., Jenkinson M., Whalen S., Golby A.J., Wells III W. Fieldmap-Free Retrospective Registration and Distortion Correction for EPI-Based Functiona Imaging. MICCAI 2008.

|-

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[Image:TGIt.gif| 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...]]

<font color="red">'''New: '''</font> Joint Segmentation of Image Ensembles via Latent Atlases T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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: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...]]

|}

Projects:LatentAtlasSegmentation

2009-09-10T18:23:25Z

Tammy:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentation of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

= Publications =
''In Press''

* Joint Segmentation of Image Ensembles via Latent Atlases, T. Riklin Raviv, K. Van Leemput, W.M. Wells III and P. Golland, MICCAI 2009.

= Key Investigators =

* MIT [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Koen Van Leemput, William M. Wells III, Polina Golland

File:TGIt.gif

2009-04-23T20:18:20Z

Tammy: uploaded a new version of "File:TGIt.gif"

File:TGIt.gif

2009-04-23T20:16:49Z

Tammy: uploaded a new version of "File:TGIt.gif"

File:TGIt.gif

2009-04-23T20:15:26Z

Tammy: uploaded a new version of "File:TGIt.gif"

Algorithm:MIT

2009-04-23T20:14:55Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[Image:TGIt.gif| 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...]]

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

|-

| |
| |

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

The goal of this project is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
[[Projects:ExpectationMaximizationSegmentation|More...]]

Algorithm:MIT

2009-04-23T20:14:26Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

| | [[Image:TGIt.gif| 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...]]

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

|-

| |
| |

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

The goal of this project is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
[[Projects:ExpectationMaximizationSegmentation|More...]]

Algorithm:MIT

2009-04-23T20:13:15Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-
| | [[Image:TGIt.gif| 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...]]

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

|-

| |
| |

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

The goal of this project is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
[[Projects:ExpectationMaximizationSegmentation|More...]]

File:TGIt.gif

2009-04-23T20:12:08Z

Tammy: uploaded a new version of "File:TGIt.gif"

Algorithm:MIT

2009-04-23T20:11:00Z

Tammy:

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008.

|-

| | [[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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New: '''</font> Automated Segmentation of Hippocampal Subﬁelds from Ultra-High Resolution In Vivo MRI, K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, and B. Fischl. Hippocampus. Accepted for Publication, 2009.

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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.
[[Projects:GroupwiseRegistration|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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

| | [[Image:TGIt.gif| 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...]]
|-

| |
| |

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

The goal of this project is to learn a probability distribution such that samples drawn from that distribution tend to look like manual segmentations of other subjects. This is useful because such a distribution can then be used as a prior in automated segmentation algorithms.
[[Projects:ExpectationMaximizationSegmentation|More...]]

Projects:LatentAtlasSegmentation

2009-04-23T19:01:19Z

Tammy:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]],
__NOTOC__
= 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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

== Contribution ==
Here we propose and demonstrate a method that does not use a set of
training images or probabilistic atlases as priors. Instead we extract an ensemble of corresponding structures
simultaneously. The evolving segmentation of the entire image set
supports each of the individual segmentations. In practice, a subset
of the model parameters, called the spatial parameters, is inferred
as part of the joint segmentation processes. These latent spatial
parameters, which can be viewed as a `dynamic atlas', are estimated exclusively
from the data at hand and a single manual segmentation.
The updated estimates of the latent atlas are used iteratively as Markov Random Field (MRF) priors on the tissue labels. The single node potentials term of the MRF model is formulated as a spatial constraint in a level-set functional for segmentation.
The main novelty of the suggested method with respect to other group-wise segmentation methods is the consistent statistically-driven variational framework for MR ensemble segmentation by estimating a latent atlas.

== Results ==
We test the proposed approach on 50 MR
brain scans. Some of the subjects in this set are diagnosed with the first episode schizophrenia or affective disorder.
The MR images (T1, 256X256X128 volume, 0.9375 X 0.9375 X 1.5 mm
voxel size) were acquired by a 1.5-T General Electric Scanner.
In addition to the MR volumes, manual
segmentations of three structures (superior temporal
gyrus, amygdala, and hippocampus) in each hemisphere were provided
for each of the 50 individuals and used to evaluate
the quality of the automatic segmentation results. MR images are preprocessed by skull stripping.
The volumes were aligned using B-spline registration.

[[Image:LatentAtlasSeg.jpg | center | 700px]]

''Three cross-sections of 3D segmentations of Hippocampus, Amygdala and Superior Temporal Gyrus in the left and the right hemispheres. Automatic segmentation is shown in red. Manual segmentation is shown in blue. Fourth column: Coronal views of the resulting atlases for each pair of structures.''

= Key Investigators =

* [[http://people.csail.mit.edu/tammy/ | Tammy Riklin Raviv]], Koen Van Leemput, William M. Wells III, Polina Golland

Projects:LatentAtlasSegmentation

2009-04-23T18:54:08Z

Tammy:

Projects:LatentAtlasSegmentation

2009-04-23T18:49:37Z

Tammy: /* Joint Segmentation of Image Ensembles via Latent Atlases */

Algorithm:MIT

2009-04-23T18:38:01Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

| | [[Image:TGIt.gif| 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...]]

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

We develop an Expectation Maximization algorithm to segment MRI Images.
[[Projects:ExpectationMaximizationSegmentation|More...]]

|}

Algorithm:MIT

2009-04-23T18:37:02Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

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

== [[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...]]

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

We develop an Expectation Maximization algorithm to segment MRI Images.
[[Projects:ExpectationMaximizationSegmentation|More...]]

|}

File:TGIt.gif

2009-04-23T18:36:13Z

Tammy:

Algorithm:MIT

2009-04-23T18:32:07Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

| | [[TGIt.jpg| 200px]]
| |

== [[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...]]

== [[Projects:ExpectationMaximizationSegmentation|Expectation Maximization Segmentation of MRI Images]] ==

Expectation Maximization Segmentation
[[Projects:ExpectationMaximizationSegmentation|More...]]

|}

Algorithm:MIT

2009-04-23T18:21:42Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

| | [[TGIt.gif| 200px]]
| |

== [[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...]]

|}

Algorithm:MIT

2009-04-23T18:19:00Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

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

== [[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...]]

|}

Algorithm:MIT

2009-04-23T18:17:19Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

| | [[Image:LatentAtlasSeg.jpg]]
| |

== [[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...]]

|}

File:LatentAtlasSeg.jpg

2009-04-23T18:13:41Z

Tammy:

Projects:LatentAtlasSegmentation

2009-04-23T17:55:02Z

Tammy:

Projects:LatentAtlasSegmentation

2009-04-23T17:51:38Z

Tammy: Created page with '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...'

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.
Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble.
The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs)
and energy minimization criteria.
We evaluate the method by segmenting 50
brain MR volumes. Segmentation accuracy for cortical and subcortical
structures approaches the quality of state-of-the-art atlas-based segmentation results,
suggesting that the ''latent atlas'' method is a reasonable alternative when
existing atlases are not compatible with the data to be processed.

Algorithm:MIT

2009-04-23T17:50:14Z

Tammy: /* MIT Projects */

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="width:15%" | [[Image:CoordinateChart.png|200px]]
| style="width:85%" |

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, M. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, P. Golland. Spherical Demons: Fast Surface Registration. MICCAI, volume 5241 of LNCS, 745--753, 2008

|-

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

== [[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: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...]]

<font color="red">'''New:'''</font> B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. Medical Image Analysis, 12(5):603--615, 2008.

|-

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

== [[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...]]

<font color="red">'''New: '''</font> Image-driven Population Analysis through Mixture-Modeling, M.R. Sabuncu, S.K. Balci, M.E. Shenton and P. Golland. IEEE Transactions on Medical Imaging. Accepted for Publication, 2009.

|-

| | [[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.
[[Projects:GroupwiseRegistration|More...]]

|-

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

== [[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...]]

<font color="red">'''New: '''</font> B.T.T. Yeo, P. Yu, P.E. Grant, B. Fischl, P. Golland. Shape Analysis with Overcomplete Spherical Wavelets. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), volume 5241 of LNCS, 468--476, 2008

B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. IEEE Transactions on Image Processing. 17(3):283--300. 2008.

|-

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

== [[Projects:HippocampalSubfieldSegmentation|Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI]] ==

In this project we develop and validate a method for fully automated segmentation of the subfields of the hippocampus in ultra-high resolution in vivo MRI. [[Projects:HippocampalSubfieldSegmentation|More...]]

<font color="red">'''New:'''</font> K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland, B. Fischl. Model-Based Segmentation of Hippocampal Subfields in Ultra-High Resolution In Vivo MRI. MICCAI 2008.

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

== [[Projects:fMRIClustering|fMRI clustering]] ==

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Projects:fMRIClustering|More...]]

<font color="red">'''New: '''</font> D. Lashkari, E. Vul, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. MICCAI 2008.

|-

| | [[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: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...]]

<font color="red">'''New:'''</font>

Mahnaz Maddah, Marek Kubicki, William M. Wells, Carl-Fredrik Westin, Martha E. Shenton and W. Eric L. Grimson, Findings in Schizophrenia by Tract-Oriented DT-MRI Analysis. MICCAI 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, W. M. Wells, Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution. MMBIA 2008.

M. Maddah, L. Zollei, W. E. L. Grimson, C-F Westin, W. M. Wells, A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. ISBI 2008.

|-

| | [[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: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...]]

|-

| |
| |

== [[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...]]

|}