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Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images

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. Overview. Introduction Incorporating Local Prior in EM-MF

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Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images

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

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

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

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

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

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

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

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

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

  11. eP(Tissue T) * P(GV[x][y][z] | Distribution of T,Bias) P(Tissue T | Position [x][y][z]) * eEnergy(WT[x][y][z] | Neighboring W) Local Prior MF-Approximation EM Algorithm + GV[x][y][z] = Grey Value at position [x][y][z] WT[x][y][z] = Weights for tissue class T at position [x][y][z] Estimating the Tissue Class WT[x][y][z]  Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images

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

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

  14. Background Skin CSV White Matter Grey Matter Right/Left Amygdala Right/Left Superior Temporalgyrus Correction of 1 Channel EM-MF-LP through Specialist Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images

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

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

  18. Overview Class Definition Class Interaction EM Settings Tabs of GUI Skill Level 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 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 Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images

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