NAMIC Wiki - User contributions [en]

Algorithm:MIT:New

2007-11-09T20:57:25Z

Ulasziyan: /* fMRI clustering */

Back to [[Algorithm:Main|NA-MIC 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).

= MIT Projects =

{|
| style="width:10%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. 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...]]

|-

| | [[Image:FoldingSpeedDetection.png|thumb|left|160px]]
| |

== [[Algorithm:MIT:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

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...]]

'''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. 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...]]

|-

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

== [[Algorithm:MIT:Multimodal Atlas |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.
[[Algorithm:MIT:Multimodal Atlas|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.
[[Algorithm:MIT:Multimodal Atlas#Publications|More...]]

|-

| | [[Image:Brain.png|thumb|left|200px]]
| |

== [[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...]]

'''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. 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...]]

|-

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

== [[Algorithm:MIT:DTI_FiberRegistration|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. [[Algorithm:MIT:DTI_FiberRegistration|More...]]

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

|-

| | [[Image:AvgResults.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:RegistrationRegularization|Effects of Registration Regularization on Segmentation Accuracy]] ==

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation.
[[Algorithm:MIT:RegistrationRegularization|More...]]

'''New:''' MICCAI 2007 Young Scientist Award in the Computational Anatomy Category.

|-

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

== [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|Shape Based Level Segmentation]] ==

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|More...]]

'''New: '''

|-

| | [[Image:Wholebrain.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_Clustering|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. [[Algorithm:MIT:DTI_Clustering|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.

|-

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

== [[Algorithm:MIT:DTI_Modeling|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. [[Algorithm:MIT:DTI_Modeling|More...]]

'''New:'''
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]]
| |

== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==

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

'''New:''' Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

|-

|-

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

== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==

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

'''New: '''

|-

| | [[Image:FMRIEvaluationchart.jpg|thumb|left|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...]]

'''New:''' 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]] ==

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...]]

'''New:''' Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

|-

| | [[Image:SerdarAffineOrig.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==

We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]

'''New:''' Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.

Algorithm:MIT:New

2007-11-09T20:56:07Z

Ulasziyan: /* Joint Registration and Segmentation of DWI Fiber Tractography */

Back to [[Algorithm:Main|NA-MIC 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).

= MIT Projects =

{|
| style="width:10%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. 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...]]

|-

| | [[Image:FoldingSpeedDetection.png|thumb|left|160px]]
| |

== [[Algorithm:MIT:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

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...]]

'''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. 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...]]

|-

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

== [[Algorithm:MIT:Multimodal Atlas |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.
[[Algorithm:MIT:Multimodal Atlas|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.
[[Algorithm:MIT:Multimodal Atlas#Publications|More...]]

|-

| | [[Image:Brain.png|thumb|left|200px]]
| |

== [[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...]]

'''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. 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...]]

|-

| | [[Image:FiberBundleReg.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_FiberRegistration|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. [[Algorithm:MIT:DTI_FiberRegistration|More...]]

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

|-

| | [[Image:AvgResults.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:RegistrationRegularization|Effects of Registration Regularization on Segmentation Accuracy]] ==

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation.
[[Algorithm:MIT:RegistrationRegularization|More...]]

'''New:''' MICCAI 2007 Young Scientist Award in the Computational Anatomy Category.

|-

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

== [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|Shape Based Level Segmentation]] ==

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|More...]]

'''New: '''

|-

| | [[Image:Wholebrain.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_Clustering|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. [[Algorithm:MIT:DTI_Clustering|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.

|-

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

== [[Algorithm:MIT:DTI_Modeling|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. [[Algorithm:MIT:DTI_Modeling|More...]]

'''New:'''
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]]
| |

== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==

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

'''New:''' Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

|-

|-

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

== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==

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

'''New: '''

|-

| | [[Image:FMRIEvaluationchart.jpg|thumb|left|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...]]

'''New:''' 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]] ==

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...]]

'''New:''' Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

|-

| | [[Image:SerdarAffineOrig.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==

We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]

'''New:''' Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.

Algorithm:MIT:New

2007-11-09T20:55:11Z

Ulasziyan: /* Joint Registration and Segmentation of DWI Fiber Tractography */

Back to [[Algorithm:Main|NA-MIC 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).

= MIT Projects =

{|
| style="width:10%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. 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...]]

|-

| | [[Image:FoldingSpeedDetection.png|thumb|left|160px]]
| |

== [[Algorithm:MIT:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

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...]]

'''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. 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...]]

|-

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

== [[Algorithm:MIT:Multimodal Atlas |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.
[[Algorithm:MIT:Multimodal Atlas|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.
[[Algorithm:MIT:Multimodal Atlas#Publications|More...]]

|-

| | [[Image:Brain.png|thumb|left|200px]]
| |

== [[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...]]

'''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. 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...]]

|-

| | [[Image:FiberBundleReg.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_FiberRegistration|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. [[Algorithm:MIT:DTI_FiberRegistration|More...]]

'''New:'''
* U. Ziyan, M. R. Sabuncu, W. E. L. Grimson, Carl-Fredrik Westin. A Robust Algorithm for Fiber-Bundle Atlas Construction. MMBIA 2007
* U. Ziyan, M. R. Sabuncu, Lauren J. O'Donnell, Carl-Fredrik Westin. Nonlinear Registration of Diffusion MR Images Based on Fiber Bundles. MICCAI 2007

|-

| | [[Image:AvgResults.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:RegistrationRegularization|Effects of Registration Regularization on Segmentation Accuracy]] ==

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation.
[[Algorithm:MIT:RegistrationRegularization|More...]]

'''New:''' MICCAI 2007 Young Scientist Award in the Computational Anatomy Category.

|-

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

== [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|Shape Based Level Segmentation]] ==

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|More...]]

'''New: '''

|-

| | [[Image:Wholebrain.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_Clustering|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. [[Algorithm:MIT:DTI_Clustering|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.

|-

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

== [[Algorithm:MIT:DTI_Modeling|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. [[Algorithm:MIT:DTI_Modeling|More...]]

'''New:'''
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]]
| |

== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==

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

'''New:''' Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

|-

|-

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

== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==

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

'''New: '''

|-

| | [[Image:FMRIEvaluationchart.jpg|thumb|left|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...]]

'''New:''' 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]] ==

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...]]

'''New:''' Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

|-

| | [[Image:SerdarAffineOrig.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==

We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]

'''New:''' Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.

Algorithm:MIT:New

2007-11-09T20:54:30Z

Ulasziyan: /* Joint Registration and Segmentation of DWI Fiber Tractography */

Back to [[Algorithm:Main|NA-MIC 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).

= MIT Projects =

{|
| style="width:10%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. 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...]]

|-

| | [[Image:FoldingSpeedDetection.png|thumb|left|160px]]
| |

== [[Algorithm:MIT:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

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...]]

'''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. 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...]]

|-

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

== [[Algorithm:MIT:Multimodal Atlas |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.
[[Algorithm:MIT:Multimodal Atlas|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.
[[Algorithm:MIT:Multimodal Atlas#Publications|More...]]

|-

| | [[Image:Brain.png|thumb|left|200px]]
| |

== [[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...]]

'''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. 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...]]

|-

| | [[Image:FiberBundleReg.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_FiberRegistration|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. [[Algorithm:MIT:DTI_FiberRegistration|More...]]

'''New: * U. Ziyan, M. R. Sabuncu, W. E. L. Grimson, Carl-Fredrik Westin. A Robust Algorithm for Fiber-Bundle Atlas Construction. MMBIA 2007
* U. Ziyan, M. R. Sabuncu, Lauren J. O'Donnell, Carl-Fredrik Westin. Nonlinear Registration of Diffusion MR Images Based on Fiber Bundles. MICCAI 2007'''

|-

| | [[Image:AvgResults.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:RegistrationRegularization|Effects of Registration Regularization on Segmentation Accuracy]] ==

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation.
[[Algorithm:MIT:RegistrationRegularization|More...]]

'''New:''' MICCAI 2007 Young Scientist Award in the Computational Anatomy Category.

|-

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

== [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|Shape Based Level Segmentation]] ==

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|More...]]

'''New: '''

|-

| | [[Image:Wholebrain.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_Clustering|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. [[Algorithm:MIT:DTI_Clustering|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.

|-

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

== [[Algorithm:MIT:DTI_Modeling|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. [[Algorithm:MIT:DTI_Modeling|More...]]

'''New:'''
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]]
| |

== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==

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

'''New:''' Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

|-

|-

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

== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==

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

'''New: '''

|-

| | [[Image:FMRIEvaluationchart.jpg|thumb|left|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...]]

'''New:''' 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]] ==

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...]]

'''New:''' Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

|-

| | [[Image:SerdarAffineOrig.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==

We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]

'''New:''' Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.

Algorithm:MIT:New

2007-11-09T20:53:10Z

Ulasziyan: /* Fiber-Tract-Bundle-based Non-Linear Registration */

Back to [[Algorithm:Main|NA-MIC 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).

= MIT Projects =

{|
| style="width:10%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|left|200px]]
| style="width:90%" |

== [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. [[Algorithm:MIT:Shape_Based_Segmentation_And_Registration|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. 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...]]

|-

| | [[Image:FoldingSpeedDetection.png|thumb|left|160px]]
| |

== [[Algorithm:MIT:ShapeAnalysisWithOvercompleteWavelets|Shape Analysis With Overcomplete Wavelets]] ==

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...]]

'''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. 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...]]

|-

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

== [[Algorithm:MIT:Multimodal Atlas |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.
[[Algorithm:MIT:Multimodal Atlas|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.
[[Algorithm:MIT:Multimodal Atlas#Publications|More...]]

|-

| | [[Image:Brain.png|thumb|left|200px]]
| |

== [[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...]]

'''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. 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...]]

|-

| | [[Image:FiberBundleReg.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_FiberRegistration|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. [[Algorithm:MIT:DTI_FiberRegistration|More...]]

'''New: '''

|-

| | [[Image:AvgResults.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:RegistrationRegularization|Effects of Registration Regularization on Segmentation Accuracy]] ==

We are interested in the effects of registration regularization on segmentation accuracy in joint registration-segmentation.
[[Algorithm:MIT:RegistrationRegularization|More...]]

'''New:''' MICCAI 2007 Young Scientist Award in the Computational Anatomy Category.

|-

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

== [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|Shape Based Level Segmentation]] ==

This class of algorithms explicitly manipulates the representation of the object boundary to fit the strong gradients in the image, indicative of the object outline. Bias in the boundary evolution towards the likely shapes improves the robustness of the segmentation results when the intensity information alone is insufficient for boundary detection. [[Algorithm:MIT:Shape_Based_Level_Set_Segmentation|More...]]

'''New: '''

|-

| | [[Image:Wholebrain.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:DTI_Clustering|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. [[Algorithm:MIT:DTI_Clustering|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.

|-

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

== [[Algorithm:MIT:DTI_Modeling|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. [[Algorithm:MIT:DTI_Modeling|More...]]

'''New:'''
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]]
| |

== [[Algorithm:MIT:DTI_Segmentation|DTI-based Segmentation]] ==

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

'''New:''' Ulas Ziyan, David Tuch, Carl-Fredrik Westin. Segmentation of Thalamic Nuclei from DTI using Spectral Clustering. Accepted to MICCAI 2006.

|-

|-

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

== [[Algorithm:MIT:DTI_StochasticTractography|Stochastic Tractography]] ==

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

'''New: '''

|-

| | [[Image:FMRIEvaluationchart.jpg|thumb|left|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...]]

'''New:''' 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]] ==

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...]]

'''New:''' Mert R Sabuncu and Polina Golland. Structural Constellations for Population Analysis of Anatomical Variability.

|-

| | [[Image:SerdarAffineOrig.jpg|thumb|left|200px]]
| |

== [[Algorithm:MIT:Groupwise_Registration#Introduction|Groupwise Registration]] ==

We are exploring algorithms for groupwise registration of medical data. [[Algorithm:MIT:Groupwise_Registration|More...]]

'''New:''' Serdar K Balci, Polina Golland, Sandy Wells, Lilla Zollei, Mert R Sabuncu and Kinh Tieu. Groupwise registration of medical data.

Projects:DTIFiberRegistration

2007-11-09T20:43:51Z

Ulasziyan:

= Joint Registration and Segmentation of DWI Fiber Tractography =

The purpose of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. We formulate a
maximum likelihood problem which the proposed method solves using a
generalized Expectation Maximization (EM) framework. Additionally,
the algorithm employs an outlier rejection and denoising strategy to
produce sharp probabilistic maps (an atlas) of certain bundles of interest.
This atlas is potentially useful for making diffusion measurements in
a common coordinate system to identify pathology related changes or
developmental trends.

= Description =

''Initial Registration''

A spatial normalization is necessary to obtain a group-wise
clustering of the resulting fibers. This initial normalization is performed
on the Fractional Anisotropy (FA)
images generated for each subject. This initial normalization aims
to remove gross differences across subjects due to global head size
and orientation. It is thus limited to a 9 parameter affine
transformation that accounts for scaling, rotation and translation.
The resulting transformations are then applied to each of the
computed fibers to map them into a common coordinate frame for
clustering.

[[Image:MIT_DTI_JointSegReg_ourapproach.jpg |thumb|400px|Our Approach]]

''Initial Fiber Clustering''

Organization of tract fibers into bundles, in the entire white
matter, reveals anatomical connections such as the corpus callosum
and corona radiata. By clustering fibers from multiple subjects into
bundles, these common white matter structures can be discovered in
an automatic way, and the bundle models can be saved with expert
anatomical labels to form an atlas. In this work,
we take advantage of automatically segmented tractography that has been labeled (as
bundles) with such an atlas for initialization.

''Joint Registration and Segmentation''

Once we obtain an initial affine registration and clustering results using the high dimensional atlas, we iteratively fine-tune the registration and clustering results using a maximum likelihood framework, which is solved through a generalized EM algorithm. For the registration we use one set of affine parameters per fiber bundle, and combine these affine registrations into a single smooth and invertable warp field using a log-Euclidian poly-affine framework. Additionally, the algorithm employs an outlier rejection and denoising strategy while
producing sharp probabilistic maps of certain bundles of interest.

We tested the registration component of this algorithm without updating the clustering with 26 major fiber bundles. The poly-affine warp fields with relatively limited number of components resulted in similar quality registrations when compared with results from a benchmark non-linear registration algorithm that was run on FA images:

{|
|
[[Image:FiberBundleReg.jpg|thumb|800px|Top Row: 3D renderings of the registered tracts of a subject (in green) and the template (in red) within 5mm of the central axial slice overlayed on the central FA slice of the template. ''Aff'' (left) stands for the FA based global affine, ''Dem'' (middle) for the demons algorithm and ''PA'' (right) for the proposed framework in this work. Arrows point to an area of differing qualities of registration. Overlapping of the red and green fibers is indicative of better registration. Bottom Row: Jacobian determinant images from the central slice of the volume: Yellow represents areas with small changes in size, and the shades of red and blue represent enlargement and shrinking, respectively. The Jacobian of the global affine registration is constant. The Jacobian of the demons algorithm is smooth due to the Gaussian regularization. The Jacobian of the new algorithm reflects the underlying anatomy because of the fiber bundle-based definition of the deformation.]]
|
|}

Corpus Callosum, Cingulum and the Fornix were selected for further investigation because of the specific challenges they present. These three structures are in close proximity with each other, and that results in many mislabeled fibers when labeled using a high dimensional atlas (see figure below (left)). Their close proximity also results in a number of trajectories deviating from one structure to another. These are precisely the sorts of artifacts we wish to reduce through learning common spatial distributions of fiber bundles from a group of subjects.

{|
|
[[Image:MIT_DTI_JointSegReg_beforeandafter.jpg|thumb|1000px|Tracts from Fornix (in green) and Cingulum (in purple) bundles along with a few selected tracts from Corpus Callosum (in black) as labeled using the high dimensional atlas (left) and after the EM algorithm with tract cuts (right). The tractography noise is evident in the images on the left as tracts deviating from one bundle to another. Also, these images contain instances where the high dimensional atlas failed to label the tracts correctly. The EM algorithm is able to remove the segments of tract bundles that are not consistent from subject to subject.]]
|
|}

We also constructed two different atlases to compare the effects of labeling algorithms on the quality of resulting group maps. The first one is constructed using the initial labels from the high dimensional atlas. A second one is built using the proposed algorithm:

{|
|
[[Image:MIT_DTI_JointSegReg_atlas2D.jpg|thumb|600px|Spatial distributions of Corpus Callosum, Cingulum and Fornix bundles from three single slices overlaid on their corresponding FA images. These maps are constructed using two different methods. a)High dimensional atlas, c) Proposed algorithm. The colorbars indicate the probability of each voxel in the spatial distribution of the corresponding fiber bundle. Note that the probabilities become higher in the central regions of the bundles and the number of sporadical voxels with non-zero probabilities decrease from left to right, indicating a sharper atlas through better registration and more consistent labeling of the subjects.]]
|
|}

[[Image:MIT_DTI_JointSegReg_atlas3D.jpg|thumb|400px|Isoprobability surfaces of the spatial distributions of Fornix (in green) and Cingulum (in purple) bundles constructed from 15 subjects using the EM algorithm with tract cut operations. A few selected tracts from Corpus Callosum (in black) are also drawn to highlight the spatial proximity of the three bundles. These spatial distributions retain very little of the tractography noise that is apparent in the individuals' tract bundles.]]

''Project Status''

* Working 3D implementation in Matlab and C.

= Key Investigators =

* MIT: Ulas Ziyan, Mert R. Sabuncu
* Harvard/BWH: Carl-Fredrik Westin, Lauren O'Donnell

= Publications =

* U. Ziyan, M. R. Sabuncu, W. E. L. Grimson, Carl-Fredrik Westin. A Robust Algorithm for Fiber-Bundle Atlas Construction. MMBIA 2007
* U. Ziyan, M. R. Sabuncu, Lauren J. O'Donnell, Carl-Fredrik Westin. Nonlinear Registration of Diffusion MR Images Based on Fiber Bundles. MICCAI 2007

Projects:DTIFiberRegistration

2007-11-09T20:35:59Z

Ulasziyan:

= Joint Registration and Segmentation of DWI Fiber Tractography =

The purpose of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. We formulate a
maximum likelihood problem which the proposed method solves using a
generalized Expectation Maximization (EM) framework. Additionally,
the algorithm employs an outlier rejection and denoising strategy to
produce sharp probabilistic maps (an atlas) of certain bundles of interest.
This atlas is potentially useful for making diffusion measurements in
a common coordinate system to identify pathology related changes or
developmental trends.

= Description =

''Initial Registration''

A spatial normalization is necessary to obtain a group-wise
clustering of the resulting fibers. This initial normalization is performed
on the Fractional Anisotropy (FA)
images generated for each subject. This initial normalization aims
to remove gross differences across subjects due to global head size
and orientation. It is thus limited to a 9 parameter affine
transformation that accounts for scaling, rotation and translation.
The resulting transformations are then applied to each of the
computed fibers to map them into a common coordinate frame for
clustering.

[[Image:MIT_DTI_JointSegReg_ourapproach.jpg |thumb|400px|Our Approach]]

''Initial Fiber Clustering''

Organization of tract fibers into bundles, in the entire white
matter, reveals anatomical connections such as the corpus callosum
and corona radiata. By clustering fibers from multiple subjects into
bundles, these common white matter structures can be discovered in
an automatic way, and the bundle models can be saved with expert
anatomical labels to form an atlas. In this work,
we take advantage of automatically segmented tractography that has been labeled (as
bundles) with such an atlas for initialization.

''Joint Registration and Segmentation''

Once we obtain an initial affine registration and clustering results using the high dimensional atlas, we iteratively fine-tune the registration and clustering results using a maximum likelihood framework, which is solved through a generalized EM algorithm. For the registration we use one set of affine parameters per fiber bundle, and combine these affine registrations into a single smooth and invertable warp field using a log-Euclidian poly-affine framework. Additionally, the algorithm employs an outlier rejection and denoising strategy while
producing sharp probabilistic maps of certain bundles of interest.

We tested the registration component of this algorithm without updating the clustering with 26 major fiber bundles. The poly-affine warp fields with relatively limited number of components resulted in similar quality registrations when compared with results from a benchmark non-linear registration algorithm that was run on FA images:

{|
|
[[Image:FiberBundleReg.jpg|thumb|800px|Top Row: 3D renderings of the registered tracts of a subject (in green) and the template (in red) within 5mm of the central axial slice overlayed on the central FA slice of the template. ''Aff'' (left) stands for the FA based global affine, ''Dem'' (middle) for the demons algorithm and ''PA'' (right) for the proposed framework in this work. Arrows point to an area of differing qualities of registration. Overlapping of the red and green fibers is indicative of better registration. Bottom Row: Jacobian determinant images from the central slice of the volume: Yellow represents areas with small changes in size, and the shades of red and blue represent enlargement and shrinking, respectively. The Jacobian of the global affine registration is constant. The Jacobian of the demons algorithm is smooth due to the Gaussian regularization. The Jacobian of the new algorithm reflects the underlying anatomy because of the fiber bundle-based definition of the deformation.]]
|
|}

Corpus Callosum, Cingulum and the Fornix were selected for further investigation because of the specific challenges they present. These three structures are in close proximity with each other, and that results in many mislabeled fibers when labeled using a high dimensional atlas (see figure below (left)). Their close proximity also results in a number of trajectories deviating from one structure to another. These are precisely the sorts of artifacts we wish to reduce through learning common spatial distributions of fiber bundles from a group of subjects.

{|
|
[[Image:MIT_DTI_JointSegReg_beforeandafter.jpg|thumb|1000px|Tracts from Fornix (in green) and Cingulum (in purple) bundles along with a few selected tracts from Corpus Callosum (in black) as labeled using the high dimensional atlas (left) and after the EM algorithm with tract cuts (right). The tractography noise is evident in the images on the left as tracts deviating from one bundle to another. Also, these images contain instances where the high dimensional atlas failed to label the tracts correctly. The EM algorithm is able to remove the segments of tract bundles that are not consistent from subject to subject.]]
|
|}

We constructed three different atlases to compare the effects of labeling algorithms on the quality of resulting group maps. The first one is constructed using the initial labels from the high dimensional atlas. A second one is built using the Expectation Maximization algorithm without the tract cut operation, and the last one is generated through the Expectation Maximization algorithm with the tract cut operation.

''Project Status''

* Working 3D implementation in Matlab and C.

= Key Investigators =

* MIT: Ulas Ziyan, Mert R. Sabuncu
* Harvard/BWH: Carl-Fredrik Westin, Lauren O'Donnell

= Publications =

* U. Ziyan, M. R. Sabuncu, W. E. L. Grimson, Carl-Fredrik Westin. A Robust Algorithm for Fiber-Bundle Atlas Construction. MMBIA 2007
* U. Ziyan, M. R. Sabuncu, Lauren J. O'Donnell, Carl-Fredrik Westin. Nonlinear Registration of Diffusion MR Images Based on Fiber Bundles. MICCAI 2007

Projects:DTIFiberRegistration

2007-11-09T20:24:33Z

Ulasziyan:

Projects:DTIFiberRegistration

2007-11-09T20:18:14Z

Ulasziyan:

File:MIT DTI JointSegReg ourapproach.jpg

2007-11-09T20:15:28Z

Ulasziyan:

File:MIT DTI JointSegReg beforeandafter.jpg

2007-11-09T20:15:17Z

Ulasziyan:

File:MIT DTI JointSegReg atlas3D.jpg

2007-11-09T20:14:54Z

Ulasziyan:

File:MIT DTI JointSegReg atlas2D.jpg

2007-11-09T20:14:41Z

Ulasziyan:

Projects:DTIFiberRegistration

2007-11-09T20:05:35Z

Ulasziyan:

Projects:DTIFiberRegistration

2007-11-09T19:54:13Z

Ulasziyan:

= Joint Registration and Segmentation of DWI Fiber Tractography =

The purpose of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. We formulate a
maximum likelihood problem which the proposed method solves using a
generalized Expectation Maximization (EM) framework. Additionally,
the algorithm employs an outlier rejection and denoising strategy to
produce sharp probabilistic maps (an atlas) of certain bundles of interest.
This atlas is potentially useful for making diffusion measurements in
a common coordinate system to identify pathology related changes or
developmental trends.

= Description =

''Initial Registration''

A spatial normalization is necessary to obtain a group-wise
clustering of the resulting fibers. This initial normalization is performed
on the Fractional Anisotropy (FA)
images generated for each subject. This initial normalization aims
to remove gross differences across subjects due to global head size
and orientation. It is thus limited to a 9 parameter affine
transformation that accounts for scaling, rotation and translation.
The resulting transformations are then applied to each of the
computed fibers to map them into a common coordinate frame for
clustering.

''Initial Fiber Clustering''

Organization of tract fibers into bundles, in the entire white
matter, reveals anatomical connections such as the corpus callosum
and corona radiata. By clustering fibers from multiple subjects into
bundles, these common white matter structures can be discovered in
an automatic way, and the bundle models can be saved with expert
anatomical labels to form an atlas. In this work,
we take advantage of automatically segmented tractography that has been labeled (as
bundles) with such an atlas for initialization.

''Joint Registration and Segmentation''

Once we obtain an initial affine registration and clustering results using the high dimensional atlas, we iteratively fine-tune the registration and clustering results using a maximum likelihood framework, which is solved through a generalized EM algorithm. For the registration we use one set of affine parameters per fiber bundle, and combine these affine registrations into a single smooth and invertable warp field using a log-Euclidian poly-affine framework. Additionally, the algorithm employs an outlier rejection and denoising strategy while
producing sharp probabilistic maps of certain bundles of interest.

We tested the registration component of this algorithm without updating the clustering with 26 major fiber bundles. The results are shown in following image:

[[Image:FiberBundleReg.jpg]]

Top Row: 3D renderings of the registered tracts of a
subject (in green) and the template (in red) within 5mm of the
central axial slice overlayed on the central FA slice of the
template. ''Aff'' (left) stands for the FA based global affine,
''Dem'' (middle) for the demons algorithm and ''PA'' (right) for the
proposed framework in this work. Arrows point to an
area of differing qualities of registration. Overlapping of the red and
green fibers is indicative of better registration. Bottom Row:
Jacobian determinant images from the central slice of the volume:
Yellow represents areas with small changes in size, and the shades
of red and blue represent enlargement and shrinking, respectively.
The Jacobian of the global affine registration is constant. The
Jacobian of the demons algorithm is smooth due to the Gaussian
regularization. The Jacobian of the new algorithm reflects
the underlying anatomy because of the fiber bundle-based definition
of the deformation.

= Description =

Collaborators: Ulas Ziyan (MIT), Lauren O'Donnell (BWH), Mert Sabuncu (MIT), Carl-Fredrik Westin (BWH)

Image registration is necessary to compare and combine information
from a group of subjects. Affine registration
(e.g. Talairach normalization) is common for group analysis, but
this typically yields poor alignment accuracy in certain local
regions of interest.

To address this, there has been interest in
developing nonlinear registration methods. In nonlinear
registration, the description and regularization of the deformation
is critical, since an unconstrained registration algorithm suffers
form the potential problem of overfitting to the images, which may
undermine consecutive analyses. A popular approach is to employ a
model-based regularization which may have no biological meaning.
Yet, we believe that the regularization and description of the
deformation should be grounded in the application.

In this project, we
explore the use of anatomical structures (called fiber
bundles) extracted from Diffusion MR Images of a group of subjects
for regularizing a non-linear registration algorithm.

'''Segmentation'''

Organization of tract fibers into bundles, in the entire white
matter, reveals anatomical connections such as the corpus callosum
and corona radiata. By clustering fibers from multiple subjects into
bundles, these common white matter structures can be discovered in
an automatic way, and the bundle models can be saved with expert
anatomical labels to form an atlas. In this work,
we take advantage of automatically segmented tractography that has been labeled (as
bundles) with such an atlas.

'''Registration of Fiber Bundles'''

To align the bundles from two subjects, we utilize the corresponce information
from the segmentation results in order to compute a non-linear warp field. More details
will be posted once this work is accepted for publication.

[[Image:FiberBundleReg.jpg]]

Top Row: 3D renderings of the registered tracts of a
subject (in green) and the template (in red) within 5mm of the
central axial slice overlayed on the central FA slice of the
template. ''Aff'' (left) stands for the FA based global affine,
''Dem'' (middle) for the demons algorithm and ''PA'' (right) for the
proposed framework in this work. Arrows point to an
area of differing qualities of registration. Overlapping of the red and
green fibers is indicative of better registration. Bottom Row:
Jacobian determinant images from the central slice of the volume:
Yellow represents areas with small changes in size, and the shades
of red and blue represent enlargement and shrinking, respectively.
The Jacobian of the global affine registration is constant. The
Jacobian of the demons algorithm is smooth due to the Gaussian
regularization. The Jacobian of the new algorithm reflects
the underlying anatomy because of the fiber bundle-based definition
of the deformation.

== Publications ==

Submitted for publication.

 

== Software ==

The algorithms now are implemented in matlab.