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Unified Framework for Automatic Segmentation, Probabilistic Atlas Construction, Registration and Clustering of Brain MR Images Annemie Ribbens Jeroen Hermans, Frederik Maes, Dirk Vandermeulen, Paul Suetens

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Introduction Computeraided diagnosis

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Introduction Segmentation

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Introduction Atlas & Atlas-to-image registration

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Population Specific Atlases Introduction

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Atlas Construction Images I Atlas previous iteration Registrations

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Images I New Atlas Deformed images Averaging

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Introduction Computer aided-diagnosis Segmentation Prob. Atlases Registration

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Framework Aspects: Segmentation Clustering (i.e. computer-aided diagnosis) (+ Localization of cluster specific morphological differences) Groupwise registration (nonrigid probabilistic atlases per cluster) Atlas-to-image registration Advantages: Less prior information necessary Cooperation Statistical framework convergence

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Framework Segmentation Atlas formation & Clustering Atlas-to-image registration

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Framework: model Y = intensities Image i K = tissue classes number of Gaussians

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Framework: model Atlas t (Gray matter map) Image i (Gray matter map)

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Framework: model Uniform prior for all voxels in an image

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Framework: model Deformations G1G1 G2G2

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Framework: MAP MAP: Jensens inequality Expectation maximization framework

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Framework: EM algorithm: E-step Gaussian mixture model Uniform prior on the cluster memberships i = images j = voxels k = tissue classes t = clusters Per cluster: atlas deformed towards image Gaussian prior on the deformations of each cluster

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Framework: EM Posterior Posterior = (clustering) * (segmentation using the atlas of the same cluster) Clustering = probability that voxel j of image i belongs to cluster t = sum over all tissue classes of the posterior = (prior of clustering) * (atlas is sharp & close to intensity model) * (subject specific registration close to groupwise) Segmentation = probability that a voxel belongs to a certain tissue class = sum over all clusters of the posterior = weighted sum of the segmentations using a specific atlas

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Framework: EM algorithm: M-step Maximum likelihood Q-function parameters All solutions close form (except registration) Solutions (e.g. atlas) ~ literature

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Framework: EM algorithm: M-step Gaussian mixture parameters: Atlas Prior cluster memberships No closed form solution Spatial regularization Viscous fluid model on derivative Groupwise registration Atlas-to-image registration Weighting terms per voxel

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w1 w8

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Experiments Brainweb data: 20 simulated normal images One cluster Segmentation & Atlas: Dice =

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Experiments 8 brain MR images of healthy persons (normals) 8 brain MR images of Huntington disease patients (HD) Cluster memberships: all correctly classified

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Conclusion Statistical framework combining: Segmentation Clustering Atlas construction per cluster (weighted) Registration Convergence & cooperation & less prior information needed Validation promising Cluster specific morphological differences are found Easily extendable to incorporate clinical/spatial prior knowledge