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Annemie Ribbens Jeroen Hermans, Frederik Maes, Dirk Vandermeulen, Paul Suetens

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. Introduction. Computer–aided diagnosis. Introduction. Segmentation. Introduction.

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Annemie Ribbens Jeroen Hermans, Frederik Maes, Dirk Vandermeulen, Paul Suetens

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  1. 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

  2. Introduction Computer–aided diagnosis

  3. Introduction Segmentation

  4. Introduction Φ Atlas & Atlas-to-image registration

  5. Introduction Population Specific Atlases

  6. Introduction Images I Registrations Atlas previous iteration Atlas Construction

  7. Images I Deformed images New Atlas Averaging

  8. Introduction Computer aided-diagnosis Segmentation Prob. Atlases Registration

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

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

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

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

  13. Framework: model Uniform prior for all voxels in an image

  14. Framework: model G1 Deformations G2

  15. Framework: MAP MAP: Jensen’s inequality Expectation maximization framework

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

  17. 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

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

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

  20. w8 w8 w1 w1

  21. Experiments Dice = • Brainweb data: • 20 simulated normal images • One cluster  Segmentation & Atlas:

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

  23. 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

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