1. 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

2. Introduction
Computer–aided diagnosis

3. Introduction
Segmentation

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

5. Introduction
Population Specific Atlases

6. Introduction Atlas Construction Images I Registrations
Atlas previous iteration
Registrations

7. Images I
Deformed images
New Atlas
Averaging

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

9. Framework Aspects: Advantages: 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

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 G2
Deformations

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

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

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
Gaussian mixture parameters:
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

20. w1 w8

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

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

25. 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