NAMIC Wiki - User contributions [en]

Projects:BayesianMRSegmentation

2009-09-10T21:06:17Z

Koen: /* Bayesian Segmentation of MRI Images */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for segmenting MRI images that automatically adapts to different acquisition sequences. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

= Application to Hippocampal Subfield Segmentation =

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

= Integration into Slicer =

We are currently working on integrating our method into 3D Slicer. Our aim is to provide an implementation that is fast and intuitive enough to be useful in hospital environments.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Algorithm:MIT

2009-09-10T21:05:39Z

Koen: /* Bayesian Segmentation of MRI Images */

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...]]

'''New: ''' 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.

'''New: ''' 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:TetrahedralAtlasWarp.gif‎ |210px]]
| style="width:85%" |

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

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

'''New: ''' 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, vol. 19, no. 6, pp. 549-557, June 2009

'''New: ''' Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 822-837, June 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...]]

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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...]]

'''New: ''' A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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

'''New: '''
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.

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

'''New: ''' Asymmetric Image-Template Registration, M.R. Sabuncu, B.T. Thomas Yeo, T. Vercauteren, K. Van Leemput, P. Golland. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

[[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

2009-09-10T21:04:35Z

Koen:

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...]]

'''New: ''' 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.

'''New: ''' 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:TetrahedralAtlasWarp.gif‎ |210px]]
| style="width:85%" |

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

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

'''New: ''' 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, vol. 19, no. 6, pp. 549-557, June 2009

'''New: ''' Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 822-837, June 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...]]

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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...]]

'''New: ''' A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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

'''New: '''
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.

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

'''New: ''' Asymmetric Image-Template Registration, M.R. Sabuncu, B.T. Thomas Yeo, T. Vercauteren, K. Van Leemput, P. Golland. To appear in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2009

[[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...]]

|}

File:TetrahedralAtlasWarp.gif

2009-09-10T21:01:27Z

Koen:

Algorithm:MIT

2009-09-10T19:15:37Z

Koen: /* Bayesian Segmentation of MRI Images */

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...]]

'''New: ''' 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.

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

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

'''New: ''' 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, vol. 19, no. 6, pp. 549-557, June 2009

'''New: ''' Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 822-837, June 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...]]

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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...]]

'''New: ''' A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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

'''New: '''
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

2009-09-10T19:15:06Z

Koen: /* Bayesian Segmentation of MRI Images */

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...]]

'''New: ''' 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.

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

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

'''New: ''' 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, vol. 19, no. 6, pp. 549-557, June 2009

Encoding Probabilistic Atlases Using Bayesian Inference, K. Van Leemput. IEEE Transactions on Medical Imaging, vol. 28, no. 6, pp. 822-837, June 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...]]

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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...]]

'''New: ''' A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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

'''New: '''
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

2009-09-10T19:13:49Z

Koen: /* Bayesian Segmentation of MRI Images */

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...]]

'''New: ''' 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.

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

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

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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...]]

'''New: ''' A. Venkataraman, K.R.A. Van Dijk, R.L. Buckner, P. Golland. Exploring Functional Connectivity in fMRI via Clustering. ICASSP 2009.

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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

'''New: '''
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...]]

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

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

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

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

2009-09-10T19:12:44Z

Koen: /* Bayesian Segmentation of MRI Images */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images that automatically adapts to different acquisition sequences. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

= Application to Hippocampal Subfield Segmentation =

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

= Integration into Slicer =

We are currently working on integrating our method into 3D Slicer. Our aim is to provide an implementation that is fast and intuitive enough to be useful in hospital environments.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T19:10:18Z

Koen: /* Bayesian Segmentation of MRI Images */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images that is fully adaptive to the acquisition sequence used. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

= Application to Hippocampal Subfield Segmentation =

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

= Integration into Slicer =

We are currently working on integrating our method into 3D Slicer. Our aim is to provide an implementation that is fast and intuitive enough to be useful in hospital environments.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T19:00:34Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

= Application to Hippocampal Subfield Segmentation =

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

= Integration into Slicer =

We are currently working on integrating our method into 3D Slicer. Our aim is to provide an implementation that is fast and intuitive enough to be useful in hospital environments.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T18:58:55Z

Koen: /* Integration into Slicer */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

== Application to Hippocampal Subfield Segmentation ==

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

== Integration into Slicer ==

We are currently working on integrating our method into 3D Slicer. Our aim is to provide an implementation that is fast and intuitive enough to be useful in hospital environments.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T18:57:38Z

Koen: /* Integration of the Segmentation Methodology into Slicer */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

== Application to Hippocampal Subfield Segmentation ==

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

== Integration into Slicer ==

We are currently working on integrating our method into 3D Slicer. Our aim is to provide an implementation that is fast and intuitive enough to be accepted in a hospital environment.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T18:52:31Z

Koen: /* Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

== Application to Hippocampal Subfield Segmentation ==

We have used our technique to automatically segment several subfields of the hippocampus directly from ultra-high resolution ''in vivo'' MRI data. Recent developments in MR data acquisition technology have started to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

We have validated our technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

== Integration of the Segmentation Methodology into Slicer ==

Segmentation algorithms based on the Expectation Maximization (EM)
theory have proved themselves capable of results of high
quality. Generally such results were obtained by carefully optimizing
the parameters for a specific MRI protocol and a specific anatomical
region. Besides the segmentation of a standard size MRI scan often
requires a processing time in the order of minutes or hours. Because
of these contraints, EM algorithms have found a limited usability in
the clinical environment. Our project aims at addressing these issues
and designing a new framework that would be easily trackable by a
clinician. The background of our team encompasses Computer Science and
Radiology. Our focus will be threefold, first to identify the bottlenecks of existing EM
algorithms, second to validate the quality of our method on a
collection of real life scans, and finally to provide an
implementation intuitive enough that it could be accepted in the
hospital and therefore make a difference for the treatment of the
patient.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T18:46:22Z

Koen: /* Bayesian Segmentation of MRI Images */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

== Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI ==

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with
error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

== Integration of the Segmentation Methodology into Slicer ==

Segmentation algorithms based on the Expectation Maximization (EM)
theory have proved themselves capable of results of high
quality. Generally such results were obtained by carefully optimizing
the parameters for a specific MRI protocol and a specific anatomical
region. Besides the segmentation of a standard size MRI scan often
requires a processing time in the order of minutes or hours. Because
of these contraints, EM algorithms have found a limited usability in
the clinical environment. Our project aims at addressing these issues
and designing a new framework that would be easily trackable by a
clinician. The background of our team encompasses Computer Science and
Radiology. Our focus will be threefold, first to identify the bottlenecks of existing EM
algorithms, second to validate the quality of our method on a
collection of real life scans, and finally to provide an
implementation intuitive enough that it could be accepted in the
hospital and therefore make a difference for the treatment of the
patient.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

Projects:BayesianMRSegmentation

2009-09-10T18:45:31Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__
= Bayesian Segmentation of MRI Images =

The aim of this project is to develop, implement, and validate a generic method for automatically segmenting MRI images. Towards this end, we design parametric computational models of how MRI images are generated, and then use these models to obtain automated segmentations in a Bayesian framework.

The model we have developed incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout an image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The models also include a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, estimating MRI intensity inhomogeneities corrupting the image, as well as finding the mean intensity and the intensity variance for each of the structures to be segmented. Once these parameters are estimated, the most probable image segmentation is obtained.

== Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI ==

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with
error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

== Integration of the Segmentation Methodology into Slicer ==

Segmentation algorithms based on the Expectation Maximization (EM)
theory have proved themselves capable of results of high
quality. Generally such results were obtained by carefully optimizing
the parameters for a specific MRI protocol and a specific anatomical
region. Besides the segmentation of a standard size MRI scan often
requires a processing time in the order of minutes or hours. Because
of these contraints, EM algorithms have found a limited usability in
the clinical environment. Our project aims at addressing these issues
and designing a new framework that would be easily trackable by a
clinician. The background of our team encompasses Computer Science and
Radiology. Our focus will be threefold, first to identify the bottlenecks of existing EM
algorithms, second to validate the quality of our method on a
collection of real life scans, and finally to provide an
implementation intuitive enough that it could be accepted in the
hospital and therefore make a difference for the treatment of the
patient.

= Key Investigators =

* MIT: Koen Van Leemput, Sylvain Jaume, Polina Golland
* BWH: Steve Pieper, Ron Kikinis

= Publications =
''In Print''

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

''In Press''

* 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.

File:MITHippocampalSubfieldSegmentation.png

2009-04-23T18:47:10Z

Koen: uploaded a new version of "File:MITHippocampalSubfieldSegmentation.png"

Algorithm:MIT

2009-04-23T18:40:25Z

Koen: /* Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI */

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...]]

'''New: ''' 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...]]

'''New:''' 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...]]

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New: ''' 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.

'''New: ''' 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...]]

'''New: ''' 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...]]

'''New:'''

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...]]

|-

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

|}

Projects:BayesianMRSegmentation

2009-04-23T18:35:20Z

Koen: /* Bayesian modeling and inference */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC____

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Segmentation Methodology =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the most probable image segmentation is obtained.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with
error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =
''In Print''

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

''In Press''

'''New: ''' 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.

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

Projects:BayesianMRSegmentation

2009-04-23T18:32:30Z

Koen: /* Results */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC____

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the most probable image segmentation is obtained.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of 10 individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in nine subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 10 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with
error bars that indicate the standard errors around the mean. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from nine manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in nine subjects, warped onto the 10th subject shown in figure 1. The colors are as in figure 1.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =
''In Print''

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

''In Press''

'''New: ''' 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.

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

File:MITHippocampalSubfieldSegmentationAtlas.png

2009-04-23T18:26:08Z

Koen: uploaded a new version of "File:MITHippocampalSubfieldSegmentationAtlas.png"

File:MITHippocampalSubfieldSegmentationQuantitative.png

2009-04-23T18:25:28Z

Koen: uploaded a new version of "File:MITHippocampalSubfieldSegmentationQuantitative.png"

File:MITHippocampalSubfieldSegmentationQualitative.png

2009-04-23T18:23:21Z

Koen: uploaded a new version of "File:MITHippocampalSubfieldSegmentationQualitative.png"

Projects:BayesianMRSegmentation

2009-04-23T18:06:52Z

Koen: /* Publications */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC____

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the most probable image segmentation is obtained.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =
''In Print''

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

''In Press''

'''New: ''' 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.

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

Projects:BayesianMRSegmentation

2009-04-23T17:55:13Z

Koen: /* Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC____

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in ''vivo'' neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the most probable image segmentation is obtained.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =
''In Print''

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

''In Press''

Projects:BayesianMRSegmentation

2009-04-23T17:50:00Z

Koen: /* Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI */

Back to [[NA-MIC_Internal_Collaborations:StructuralImageAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC____

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research.

The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the most probable image segmentation is obtained.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =
''In Print''

*[http://www.na-mic.org/pages/Special:Publications?text=Projects%3AHippocampalSubfieldSegmentation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]

''In Press''

Projects:BayesianMRSegmentation

2008-05-16T22:59:20Z

Koen: /* Bayesian modeling and inference */

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the most probable image segmentation is obtained.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

* 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. MICCAI2008 (accepted)

Projects:BayesianMRSegmentation

2008-05-16T22:57:57Z

Koen: /* Bayesian modeling and inference */

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build a parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

* 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. MICCAI2008 (accepted)

Projects:BayesianMRSegmentation

2008-05-16T22:57:22Z

Koen: /* Bayesian modeling and inference */

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of the model that are most probable in light of the data. The parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

* 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. MICCAI2008 (accepted)

Algorithm:MIT

2008-05-16T22:53:25Z

Koen: /* Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= 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.

= MIT Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|200px]]
| style="width:85%" |

== [[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...]]

'''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 

|-

| | [[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...]]

'''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.'''

|-

| | [[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...]]

'''New: ''' M.R. Sabuncu, Serdar K. Balci and Polina Golland. Discovering Modes of an Image Population through Mixture. Accepted to MICCAI 2008.
'''In Press:''' 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.

|-

| | [[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...]]

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.

|-

| | [[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...]]

'''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.

|-

| | [[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...]]

'''New: ''' D. Lashkari, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. Accpeted in 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...]]

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

|-

| | [[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...]]

'''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|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 discription in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|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.

|-

| | [[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...]]

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

|-

| | [[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...]]

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

|-

| | [[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...]]

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

|-

| | [[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...]]

'''New:''' 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. MICCAI2008 (accepted)

|}

Algorithm:MIT

2008-05-16T22:39:40Z

Koen: /* MIT Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= 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.

= MIT Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|200px]]
| style="width:85%" |

== [[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...]]

'''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 

|-

| | [[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...]]

'''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.'''

|-

| | [[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...]]

'''New: ''' M.R. Sabuncu, Serdar K. Balci and Polina Golland. Discovering Modes of an Image Population through Mixture. Accepted to MICCAI 2008.
'''In Press:''' 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.

|-

| | [[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...]]

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.

|-

| | [[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...]]

'''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.

|-

| | [[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...]]

'''New: ''' D. Lashkari, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. Accpeted in 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...]]

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

|-

| | [[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...]]

'''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|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 discription in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|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.

|-

| | [[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...]]

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

|-

| | [[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...]]

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

|-

| | [[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...]]

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

|-

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

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

'''New:''' 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. MICCAI2008 (accepted)

|}

File:MITHippocampalSubfieldSegmentation.png

2008-05-16T22:37:58Z

Koen:

Algorithm:MIT

2008-05-16T22:31:48Z

Koen: /* Model-Based Segmentation of Hippocampal Subﬁelds in In Vivo MRI */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= 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.

= MIT Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|200px]]
| style="width:85%" |

== [[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...]]

'''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 

|-

| | [[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...]]

'''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.'''

|-

| | [[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...]]

'''New: ''' M.R. Sabuncu, Serdar K. Balci and Polina Golland. Discovering Modes of an Image Population through Mixture. Accepted to MICCAI 2008.
'''In Press:''' 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.

|-

| | [[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...]]

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.

|-

| | [[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...]]

'''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.

|-

| | [[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...]]

'''New: ''' D. Lashkari, N. Kanwisher, P. Golland. Discovering Structure in the Space of Activation
Profiles in fMRI. Accpeted in 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...]]

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

|-

| | [[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...]]

'''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|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 discription in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|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.

|-

| | [[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...]]

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

|-

| | [[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...]]

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

|-

| | [[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...]]

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

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

'''New:''' 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. MICCAI2008 (accepted)

|}

Projects:BayesianMRSegmentation

2008-05-16T22:25:21Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

* 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. MICCAI2008 (accepted)

Projects:BayesianMRSegmentation

2008-05-16T22:21:53Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table align="center">
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

Projects:BayesianMRSegmentation

2008-05-16T22:17:58Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|600px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

<table>
<tr>
<td>
[[Image:MITHippocampalSubfieldSegmentationQuantitative.png|thumb|center|300px|Fig 2. Dice overlap measures (top) and relative volume differences (bottom) between automated and manual segmentations. The colors are as in figure 1.]]
</td>
<td>
[[Image:MITHippocampalSubfieldSegmentationAtlas.png|thumb|center|300px|Fig 3. Mesh-based probabilistic atlas, derived from manual delineations in 4 subjects, warped onto the 5th subject shown in figure 1. Bright and dark intensities correspond to high and low prior probability for subiculum, respectively.]]
</td>
</tr>
</table>

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

Projects:BayesianMRSegmentation

2008-05-16T22:08:02Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. The lower half of the figure shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

[[Image:MITHippocampalSubfieldSegmentationQualitative.png|thumb|center|300px|Fig 1. From left to right: ultra-high resolution MRI data, manual delineations, and corresponding automated segmentations.]]

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

Projects:BayesianMRSegmentation

2008-05-16T21:51:17Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices. The upper half of figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects, whereas the lower half shows, for each structure, the volume differences between the automated and manual segmentations relative to their mean volumes. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

File:MITHippocampalSubfieldSegmentationAtlas.png

2008-05-16T21:48:44Z

Koen:

File:MITHippocampalSubfieldSegmentationQuantitative.png

2008-05-16T21:47:54Z

Koen:

File:MITHippocampalSubfieldSegmentationQualitative.png

2008-05-16T21:46:27Z

Koen:

Projects:BayesianMRSegmentation

2008-05-16T21:29:57Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices, and figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects. An example of our mesh-based probabilistic atlas, derived from 4 manually labeled hippocampi, is shown in figure 3.

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

Projects:BayesianMRSegmentation

2008-05-16T21:25:45Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure 1 compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices, and figure 2 shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects.

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

Projects:BayesianMRSegmentation

2008-05-16T21:24:39Z

Koen:

Back to [[NA-MIC_Internal_Collaborations:fMRIAnalysis|NA-MIC Collaborations]], [[Algorithm:MIT|MIT Algorithms]]
__NOTOC__

= Model-Based Segmentation of Hippocampal Subfields in In Vivo MRI =

Recent developments in MR data acquisition technology are starting to yield images that show anatomical features of the hippocampal formation at an unprecedented level of detail, providing the basis for hippocampal subfield measurement. Because of the role of the hippocampus in human memory and its implication in a variety of disorders and conditions, the ability to reliably and efficiently quantify its subfields through in vivo neuroimaging is of great interest to both basic neuroscience and clinical research. The aim of this project is to develop and validate a fully-automated method for segmenting the hippocampal subfields in ultra-high resolution
MRI data.

= Bayesian modeling and inference =

We use a Bayesian modeling approach, in which we build an parametric computational model of how an MRI image around the hippocampal area is generated. The model incorporates a ''prior'' distribution that makes predictions about where neuroanatomical labels typically occur throughout the image, and is based on a generalization of probabilistic atlases that uses a deformable, compact tetrahedral mesh representation. The model also includes a ''likelihood'' distribution that predicts how a label image, where each voxel is assigned a unique neuroanatomical label, translates into an MRI image, where each voxel has an intensity.

Given an image to be segmented, we first estimate the parameters of our model that are most probable in light of the data. This parameter estimation involves finding the deformation that optimally warps the mesh-based probabilistic atlas onto the image under study, as well as finding the mean intensity and the intensity variance for each of the hippocampal subfields in the image. Once these parameters are estimated, the model is used to obtain the most probable image segmentation.

= Results =

We have validated the proposed technique by comparing our automated segmentation results with corresponding manual delineations in ultra-high resolution MRI scans (voxel size 0.38x0.38x0.80mm^3) of five individuals. For each of seven structures of interest (fimbria, CA1, CA2/3, CA4/DG, presubiculum, subiculum, and hippocampal fissure), we calculated the Dice overlap coefficient, defined as the volume of overlap between the automated and manual segmentation divided by their mean volume. We used a leave-one-out cross-validation strategy, in which we built an atlas mesh from the delineations in 4 subjects, and used this to segment the image of the remaining subject. We repeated this process for each of the 5 subjects, and compared the automated segmentation results with the corresponding manual delineations.

Figure~\ref{fig:qualitative} compares the manual and automated segmentation results qualitatively on a set of cross-sectional slices, and figure~\ref{fig:quantitative} shows the average Dice overlap measure for each of the structures of interest, along with the minimum and maximum across the 5 subjects.

= Key Investigators =

* MIT Algorithms: Koen Van Leemput, Polina Golland

= Publications =

Projects:BayesianMRSegmentation

2008-05-16T21:10:44Z

Koen:

Projects:BayesianMRSegmentation

2008-05-16T21:09:12Z

Koen:

Projects:BayesianMRSegmentation

2008-05-16T21:06:16Z

Koen:

Projects:BayesianMRSegmentation

2008-05-16T20:55:48Z

Koen:

Algorithm:MIT

2008-05-16T20:45:28Z

Koen:

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= 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.

= MIT Projects =

{| cellpadding="10"
| style="width:15%" | [[Image:Progress_Registration_Segmentation_Shape.jpg|200px]]
| style="width:85%" |

== [[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...]]

'''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 

|-

| | [[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...]]

'''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.'''

|-

| | [[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...]]

'''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.

|-

| | [[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...]]

'''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.

|-

| | [[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...]]

'''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.

|-

| | [[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...]]

'''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.

|-

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

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

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== [[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...]]

'''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.

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== [[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 discription in clustering and statistical analysis of fiber tracts. [[Projects:DTIModeling|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.

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== [[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...]]

'''New:''' 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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== [[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...]]

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

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== [[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...]]

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== [[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...]]

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

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

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Projects:BayesianMRSegmentation

2008-05-16T20:44:49Z

Koen:

Projects:BayesianMRSegmentation

2008-05-16T20:42:28Z

Koen: