1. Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images By K.M. Pohl, W.M. Wells, A. Guimond, K. Kasai, M.E. Shenton, R. Kikinis, W.E.L. Grimson, and S.K. Warfield Email: pohl@mit.edu

2. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 2 - Overview Introduction Incorporating Local Prior in EM-MF Current Implementation – Tools and Tricks Possible Advancements Conclusion

3. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 3 - Goal SPGR T2W Tissue Atlases The Magic Automatic Segmenter

4. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 4 - EM-MF Algorithm M-Step E-Step Smooth Bias Image Correct Intensities MF-Step Regularize Weights Estimate Tissue Probability Label Map

5. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 5 - Mean Entropy Atlas

6. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 6 - Merging MEA with SPGR

7. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 7 - Bias

8. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 8 - Bias in Color

9. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 9 - 3 D View of SPGR

10. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 10 - Including Local Priors 2. Step 1. Step Brain Atlas Case Registration Probability Maps Align Atlas 3. Step M-Step E-Step Bias Correct MF-Step Estimate Label Map

11. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 11 - e P(Tissue T) * P(GV[x][y][z] | Distribution of T,Bias) EM Algorithm P(Tissue T | Position [x][y][z]) Local Prior Estimating the Tissue Class W T [x][y][z]  * e Energy(WT[x][y][z] | Neighboring W) MF-Approximation GV[x][y][z] = Grey Value at position [x][y][z] W T [x][y][z] = Weights for tissue class T at position [x][y][z] +

12. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 12 - Comparing different Segmenter Registration only EM-MF Affine EM-MF Non Rigid EM-MF 2 Channel Input - Segmenting up to 7 tissue classes

13. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 13 - 2 Channel Segmentation with Patient Case and 11 Tissue Classes

14. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 14 - Correction of 1 Channel EM-MF-LP through Specialist BackgroundCSVSkinGrey MatterWhite Matter Right/Left AmygdalaRight/Left Superior Temporalgyrus

15. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 15 - Comparing Manual to EM-MF-LP of the STG Rater ARater B EM-MF-LP

16. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 16 - Current Installation Algorithm is a VTK Filter integrated in Slicer MF Approximation: –Multi Threaded –Lookup Table for Gaussian Distribution Using several Relaxation Methods instead of the Mean Field Energy Function Multi Channel Input (SPGR, T2, PD) Train Tissue Definition, e.g. CIM, Distribution Interface to Matlab

17. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 17 - EM-MF in Slicer

18. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 18 - Tabs of GUI Overview Class DefinitionClass InteractionEM Settings Skill Level

19. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 19 - Possible Improvements Registration Step: –After each segmentation re-register case with atlas E Step –Include shape and topology information in weight calculation –Use local class interaction matrix M Step: –Use several other filters to smooth bias, e.g. Box Filter, Pascal Triangle, … –Include “trash tissue class” where pixels get assigned if all weights are low  Bias does not get corrupted

20. MICCAI’02 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images - 20 - Conclusion Made EM-MF Algorithm more robust Segmented tissue classes with overlapping gray value distributions Included spatial/atlas information into E-Step Cortex pacellation possible Future Work: Validating Segmentation