1. NA-MIC National Alliance for Medical Image Computing http://na-mic.org Segmentation Core 1-3 Meeting, May. 22-23, 2008 - SLC, UT

2. Georgia Tech/JHU Prostate Segmentation & Registration Framework Yi Gao (Georgia Tech), Allen Tannenbaum (Georgia Tech), Gabor Fichtinger (JHU)‏

3. Background Under the roadmap project: Brachytherapy Needle Positioning Robot Integration. Auto/Semiauto segmentation. Registration: –Between modalities: US/MRI –Before/during therapy

4. Segmentation Two approaches: –Random Walks(RW)‏ RW + post process Toward automatic segmentation –Spherical wavelet shape based method

5. Random walks Random Walks(RW)‏ –Result → –Less interaction –C++ code

6. Shape based method Spherical wavelet shape based method –Shape learning –ITK Spherical wavelet transformation –Shape based segmentation

7. Shape based method Spherical wavelet shape based method –Shape learning –ITK Spherical wavelet transformation –Shape based segmentation

8. Shape learning Align segmented shapes. –Registration under Similarity transform. Learn aligned shapes. –Statistical learning: PCA, KPCA, GPCA

9. Shape learning Align segmented shapes. –Registration under Similarity transform. Learn aligned shapes. –Statistical learning: PCA, KPCA, GPCA

10. Registration Common region extraction –Used as landmark Rigid/Deformable registration –Particle filter/Kalman filter –Optimal mass transportation

11. Landmark extraction Chan-Vese on manifold –Extract featured region on surface. –Feature defined by a function. Color depicts a scalar function defined on a surface.

12. Landmark extraction, cont.

13. Landmark based Registration Concave belly of prostate –Common among all prostate Used as soft-landmark in registration.

14. UNC/MIND Lesion Segmentation Marcel Prastawa (Utah), Guido Gerig (Utah), Jeremy Bockholt (MIND)‏

15. Lesion Segmentation T1T1 T2T2 before after

16. MIND Lupus Lesion

17. Iowa/MIND Bayesian Classification of Lupus Lesions Vincent A. Magnotta (Iowa), Jeremy Bockholt (MIND), Peter Pellegrino (Iowa)‏

18. Algorithm Overview Tissue classification algorithm coupled with lesion identification Required Inputs –T1, T2, and FLAIR images that have been spatially normalized and bias field corrected –Definition of the brain –Currently uses BRAINS Autoworkup pipeline to fulfill these requirements

19. Algorithm Uses K-means classification –Initial estimate of GM, WM, and CSF based on minimum, mean, and standard deviation from T1 weighted image –Kmeans segmentation into GM, WM, and CSF from T1 weighted image Lesion from FLAIR Images –Threshold FLAIR image based on mean and standard deviation within the brain –Eliminate lesion voxels adjacent to CSF –Remaining lesion voxels from the Kmeans classification are used to relabel the Kmeans labelmap with a Lesion value

20. Bayesian Classification Define exemplars for classes –Randomly sample 1000 points from GM, WM, CSF, and Lesion labels –Used to define the means and variance for the classes Define class priors –Extract each class from labelmap generated in previous step and filter with a 2mm gaussian filter Run multi-modal Bayesian classifier –T1, T2, and FLAIR images input

21. Results

22. MIT/Harvard Tissue Classification Kilian Pohl (MIT/BWH), Brad Davis (Kitware), Sylvain Bouix (Harvard), Marek Kubicki (Harvard), Martha Shenton (Harvard), Sandy Wells (BWH), Polina Golland (MIT)‏

23. Slicer 3 Module

24. EM-Segmenter Intensity normalization Structure hierarchy Registration –Atlas-to-subject –Multimodal Applications: –Tissue classification –Structure parcelation –MS lesions segmentation

25. Georgia Tech/Harvard Label Space Segmentation Jimi Malcolm (Georgia Tech), Allen Tannenbaum (Georgia Tech), Yogesh Rathi (Harvard)‏

26. Problem: Constructing an anatomical model for multiple, covarying regions - Slice from labeled brain: State of the art - Signed distance maps: develop artifacts along interface between regions, small variations on interface cause large perturbations far away - Binary vectors: background bias during registration - LogOdds: natural probabilistic interpretation, but uses the above intermediate representations thus incurring similar problems Label Space

27. Label Space: - regular simplex: - natural algebraic manipulation - direct probabilistic interpretation - unbiased toward any label

28. Label Space Experiments: - smoothing, interpolation - registration - probabilistic atlases

29. Questions?