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= Overview of MIT Algorithms =
 
= Overview of MIT Algorithms =
  
A brief overview of the MIT's algorithms goes here.  This should not be much longer than a paragraph.  Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table:  the left column is for representative images and the right column is for project overviews.  The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table.  You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).
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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.
  
[[#Shape_Based_Segmentation_and_Registration|Shape Based Segmentation and Registration]]
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Most Recent Projects:
  
[[#Effects_of_Registration_Regularization_on_Segmentation_Accuracy|Effects of Registration Regularization on Segmentation Accuracy]]
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* Segmentation
 
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**[[#Shape_Based_Segmentation_and_Registration|Shape Based Segmentation and Registration]]
[[#Multimodal_Atlas|Multimodal Atlas]]
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**[[#Effects_of_Registration_Regularization_on_Segmentation_Accuracy|Optimal Atlas Regularization in Image Segmentation]]
 
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**[[#Joint_Registration_and_Segmentation_of_DWI_Fiber_Tractography|Joint Registration and Segmentation of DWI Fiber Tractography]]
[[#Shape_Analysis_With_Overcomplete_Wavelets|Shape Analysis With Overcomplete Wavelets]]
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**[[#fMRI_clustering|fMRI clustering]]
 
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* Registration
[[#fMRI_clustering|fMRI clustering]]
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**[[#Groupwise_Registration|Groupwise Registration]]
 
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**[[#Multimodal_Atlas|Multimodal Atlas]]
[[#Joint_Registration_and_Segmentation_of_DWI_Fiber_Tractography|Joint Registration and Segmentation of DWI Fiber Tractography]]
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**[[#Effects_of_Registration_Regularization_on_Segmentation_Accuracy|Optimal Atlas Regularization in Image Segmentation]]
 
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**[[#Joint_Registration_and_Segmentation_of_DWI_Fiber_Tractography|Joint Registration and Segmentation of DWI Fiber Tractography]]
[[#Shape_Based_Level_Segmentation|Shape Based Level Segmentation]]
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* Shape Representation and Analysis
 
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**[[#Shape_Analysis_With_Overcomplete_Wavelets|Shape Analysis With Overcomplete Wavelets]]
[[#DTI_Fiber_Clustering_and_Fiber-Based_Analysis|DTI Fiber Clustering and Fiber-Based Analysis]]
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* Modeling of function
 
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**[[#fMRI_clustering|fMRI clustering]]
[[#Fiber_Tract_Modeling.2C_Clustering.2C_and_Quantitative_Analysis|Fiber Tract Modeling, Clustering and Quantitative Analysis]]
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**[[#fMRI_Detection_and_Analysis|fMRI Detection and Analysis]]
 
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* White Matter Architecture
[[#DTI-based_Segmentation|DTI-based Segmentation]]
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**[[#Joint_Registration_and_Segmentation_of_DWI_Fiber_Tractography|Joint Registration and Segmentation of DWI Fiber Tractography]]
 
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**[[#DTI_Fiber_Clustering_and_Fiber-Based_Analysis|DTI Fiber Clustering and Fiber-Based Analysis]]
[[#Stochastic_Tractography|Stochastic Tractography]]
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**[[#Fiber_Tract_Modeling.2C_Clustering.2C_and_Quantitative_Analysis|Fiber Tract Modeling, Clustering and Quantitative Analysis]]
 
 
[[#fMRI_Detection_and_Analysis|fMRI Detection and Analysis]]
 
 
 
[[#Population_Analysis_of_Anatomical_Variability|Population Analysis of Anatomical Variability]]
 
 
 
[[#Groupwise_Registration|Groupwise Registration]]
 
  
 
= MIT Projects =
 
= MIT Projects =
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This type of algorithms 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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|More...]]
 
This type of algorithms 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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|More...]]
  
<font color="red">'''New: '''</font> K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Accepted to the Special Issue of Best Selected Papers from MICCAI 06 in Medical Image Analysis [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Publications|More...]]
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<font color="red">'''New: '''</font> K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Medical Image Analysis,11(6), pp. 465-477, 2007. <b>Best Paper Award MICCAI 2006 </b> [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Publications|More...]]
  
 
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== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==
 
== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==
  
We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]
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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.
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[[Algorithm:MIT:Groupwise_Registration|More...]]
  
<font color="red">'''New:'''</font> Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.
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<font color="red">'''New:'''</font> S.K. Balci, P. Golland, M.E. Shenton, W.M. Wells III. Free-Form B-spline Deformation Model for Groupwise Registration. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 23-30, 2007.
  
  
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|-
 
|-
  
| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|center|200px]]
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| | [[Image:mit_fmri_clustering_parcellation2_xsub.png|center|150px]]
 
| |
 
| |
  
 
== [[Algorithm:MIT:fMRI Clustering |fMRI clustering]] ==
 
== [[Algorithm:MIT:fMRI Clustering |fMRI clustering]] ==
  
This type of algorithms 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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|More...]]
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In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. [[Algorithm:MIT:fMRI_Clustering|More...]]
  
<font color="red">'''New: '''</font> K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Accepted to the Special Issue of Best Selected Papers from MICCAI 06 in Medical Image Analysis [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration#Publications|More...]]
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<font color="red">'''New: '''</font> P. Golland, Y. Golland, R. Malach. Detection of Spatial Activation Patterns As Unsupervised Segmentation of fMRI Data. In Proceedings of MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, 110-118, 2007.  
 
    
 
    
 
|-
 
|-
  
| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|thumb|left|200px]]
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| | [[Image:MIT_DTI_JointSegReg_atlas3D.jpg|center|200px]]
 
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<font color="red">'''New: '''</font>
 
<font color="red">'''New: '''</font>
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|-
 
|-
  
| | [[Image:Wholebrain.jpg|thumb|left|200px]]
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| | [[Image:CingulumAllSubjectsFibers.png|center|200px]]
 
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|-
  
| | [[Image:Models.jpg|thumb|left|200px]]
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<font color="red">'''New:'''</font>  
 
<font color="red">'''New:'''</font>  
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M. Maddah, W. E. L. Grimson, S. K. Warfield, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts. Medical Image Analysis, in press.
 +
 
M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson, Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.
 
M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson, Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.
  
 
|-
 
|-
  
| | [[Image:Thalamus_algo_outline.png|thumb|left|200px]]
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| | [[Image:FMRIEvaluationchart.jpg|center|200px]]
 
| |  
 
| |  
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== [[Algorithm:MIT:fMRI_Detection|fMRI Detection and Analysis]] ==
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 +
We are exploring algorithms for improved fMRI detection and interpretation by incorporting spatial priors and anatomical information to guide the detection. [[Algorithm:MIT:fMRI_Detection|More...]]
 +
 +
<font color="red">'''New:'''</font> Wanmei Ou, Sandy Wells, Polina Golland. Bridging Spatial Regularization And Anatomical Priors in fMRI Detection. In preparation for submission to IEEE TMI.
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 +
|-
 +
 +
| | [[Image:Thalamus_algo_outline.png|center|150px]]
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| |
  
 
== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==
 
== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==
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Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Algorithm:MIT:DTI_Segmentation|More...]]
 
Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. [[Algorithm:MIT:DTI_Segmentation|More...]]
  
 +
<!--
 
<font color="red">'''New:'''</font> Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.
 
<font color="red">'''New:'''</font> Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.
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-->
  
 
|-
 
|-
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|-
 
|-
  
| | [[Image:ConnectivityMap.png|thumb|left|200px]]
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| | [[Image:ConnectivityMap.png|center|150px]]
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== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==
 
== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==
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This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume.  [[Algorithm:MIT:DTI_StochasticTractography|More...]]
 
This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume.  [[Algorithm:MIT:DTI_StochasticTractography|More...]]
  
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<!--
 
<font color="red">'''New: '''</font>
 
<font color="red">'''New: '''</font>
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-->
  
 
|-
 
|-
  
| | [[Image:FMRIEvaluationchart.jpg|thumb|left|200px]]
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| | [[Image:HippocampalShapeDifferences.gif|center|200px]]
 
| |  
 
| |  
  
== [[Algorithm:MIT:fMRI_Detection|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. [[Algorithm:MIT:fMRI_Detection|More...]]
 
 
<font color="red">'''New:'''</font> Wanmei Ou, Sandy Wells, Polina Golland. Bridging Spatial Regularization And Anatomical Priors in fMRI Detection. In preparation for submission to IEEE TMI.
 
 
|-
 
 
| | [[Image:HippocampalShapeDifferences.gif|thumb|left|200px]]
 
| |
 
  
 
== [[Algorithm:MIT:Shape_Analisys|Population Analysis of Anatomical Variability]] ==
 
== [[Algorithm:MIT:Shape_Analisys|Population Analysis of Anatomical Variability]] ==
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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. [[Algorithm:MIT:Shape_Analisys|More...]]
 
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. [[Algorithm:MIT:Shape_Analisys|More...]]
  
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<font color="red">'''New:'''</font> Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.
 
<font color="red">'''New:'''</font> Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.
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Latest revision as of 06:19, 11 April 2023

Home < Algorithm:MIT:New

Back to NA-MIC Algorithms

Overview of MIT Algorithms

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.

Most Recent Projects:

MIT Projects

Progress Registration Segmentation Shape.jpg

Shape Based Segmentation and Registration

This type of algorithms 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. More...

New: K.M. Pohl, J. Fisher, S. Bouix, M. Shenton, R. W. McCarley, W.E.L. Grimson, R. Kikinis, and W.M. Wells. Using the Logarithm of Odds to Define a Vector Space on Probabilistic Atlases. Medical Image Analysis,11(6), pp. 465-477, 2007. Best Paper Award MICCAI 2006 More...

JointRegSeg.png

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. More...

New: B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P. Golland. Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy. In Proceedings of MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, 683-691, 2007. MICCAI Young Scientist Award.

ICluster templates.gif

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. More...

New: M.R. Sabuncu, M.E. Shenton, P. Golland. Joint Registration and Clustering of Images. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 47-54, 2007. More...


GroupwiseSummary.PNG

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. More...

New: S.K. Balci, P. Golland, M.E. Shenton, W.M. Wells III. Free-Form B-spline Deformation Model for Groupwise Registration. In Proceedings of MICCAI 2007 Statistical Registration Workshop: Pair-wise and Group-wise Alignment and Atlas Formation, 23-30, 2007.



FoldingSpeedDetection.png

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 More...

New: B.T.T. Yeo, W. Ou, P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. Yeo, Ou and Golland. Accepted to the IEEE Transactions on Image Processing.

P. Yu, B.T.T. Yeo, P.E. Grant, B. Fischl, P. Golland. Cortical Folding Development Study based on Over-Complete Spherical Wavelets. In Proceedings of MMBIA: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, 2007. More...


Mit fmri clustering parcellation2 xsub.png

fMRI clustering

In this project we study the application of model-based clustering algorithms in identification of functional connectivity in the brain. More...

New: P. Golland, Y. Golland, R. Malach. Detection of Spatial Activation Patterns As Unsupervised Segmentation of fMRI Data. In Proceedings of MICCAI: International Conference on Medical Image Computing and Computer Assisted Intervention, 110-118, 2007.

MIT DTI JointSegReg atlas3D.jpg

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. More...

New: U. Ziyan, M. R. Sabuncu, W. E. L. Grimson, Carl-Fredrik Westin. A Robust Algorithm for Fiber-Bundle Atlas Construction. MMBIA 2007


CingulumAllSubjectsFibers.png

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. More...

New: Monica E. Lemmond, Lauren J. O'Donnell, Stephen Whalen, Alexandra J. Golby. Characterizing Diffusion Along White Matter Tracts Affected by Primary Brain Tumors. Accepted to HBM 2007.

Models.jpg

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 discription in clustering and statistical analysis of fiber tracts. More...

New: M. Maddah, W. E. L. Grimson, S. K. Warfield, W. M. Wells, A Unified Framework for Clustering and Quantitative Analysis of White Matter Fiber Tracts. Medical Image Analysis, in press.

M. Maddah, W. M. Wells, S. K. Warfield, C.-F. Westin, and W. E. L. Grimson, Probabilistic Clustering and Quantitative Analysis of White Matter Fiber Tracts,IPMI 2007, Netherlands.

FMRIEvaluationchart.jpg

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. More...

New: Wanmei Ou, Sandy Wells, Polina Golland. Bridging Spatial Regularization And Anatomical Priors in fMRI Detection. In preparation for submission to IEEE TMI.

Thalamus algo outline.png

DTI-based Segmentation

Unlike conventional MRI, DTI provides adequate contrast to segment the thalamic nuclei, which are gray matter structures. More...


ConnectivityMap.png

Stochastic Tractography

This work calculates posterior distributions of white matter fiber tract parameters given diffusion observations in a DWI volume. More...


HippocampalShapeDifferences.gif


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. More...