CSCE790T Medical Image Processing

1. CSCE790T Medical Image Processing University of South Carolina Department of Computer Science 3D Active Shape Models Integrating Robust Edge Identification and Statistical Shape Models

2. Overview • Introduction • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments / Results • Conclusion

3. Introduction • Collaboration with UNC departments of computer science, and psychiatry • Submitted to MICCAI 07 • Propose two new strategies to improve 3D ASM performance: • Developing a robust edge-identification algorithm to reduce the risk of detecting false edges • Integrating the edge-fitting error and statistical shape model defined by a PDM into a unified cost function

4. Introduction • Apply the proposed ASM to the challenging tasks of detecting the left hippocampus and caudate surfaces from an subset of 3D pediatric MR images • Compare its performance with a recently reported atlas based method.

5. Overview • Introduction • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments / Results • Conclusion

6. Motivation • Segmentation facilitates the discovery of diseased structures in medical images • Two neurological shape structures of interest • Caudate Nucleus • body movement and coordination • cauda (tail) • Hippocampus • memory and coordination • hippo (horse) and Kampi (curve)

7. Motivation http://www.emedicine.com/radio/topic443.htm#target2

8. Motivation http://www.sci.uidaho.edu/med532/basal.htm

9. Motivation • Hippocampus, and Caudate related to the following areas of research: • Epileptic seizures (MTS) • Alzheimer disease • Amnesic syndromes • Schizophrenia • Parkinson's disease • Huntington's disease

10. Overview • Introduction • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments / Results • Conclusion

11. General ASM Algorithm • Initial placement of point distribution model (PDM) mean shape inside image volume T (v : s, t,  ) • Generate gradient magnitude values for each voxel location in 3D image volume • while not(convergence) • Identify strongest edge for each landmark point along its search path • Using this edge information determine new ASM shape • Update PDM global transform T(s, t, ) and local transform variables • Verify new ASM shape with PDM shape space limits • If global, and local transform variables are not longer changing ASM has converged

12. Overview • Introduction • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments / Results • Conclusion

13. Robust Edge Detection • Identify boundary edges of desired surface structure inside image volume • Each edge is represented by an gradient magnitude value • Stronger edges have larger gradient magnitude values

14. Robust Edge Detection Example sagittal plane edges for hippocampus Image slice Gradient magnitude slice

15. Robust Edge Detection Example coronal plane edges for hippocampus Image slice Gradient magnitude slice

16. Robust Edge Detection • Boundary edges are identified along search paths for each landmark point • Search paths are defined by profile locations () along each landmark points normal vector

17. Robust Edge Detection • Additionally, each landmark points normal vector is determined by the surface mesh 5 2 A n6 = ¼ x (nD + nE + nF + nG) = n6/ || n6 || 1 E F B 6 C 8 D 3 G 4 7

18. Robust Edge Detection

19. Robust Edge Detection • Generally, edges detection along search paths are considered dangerous • Subject to noise • Spurious (false) edges

20. Robust Edge Detection • Propose an new neighborhood solution • Spatially consistent profile location (i) • Reduces the likelihood of an false edge

21. Overview • Introduction • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments / Results • Conclusion

22. Unified Cost Function • Traditionally each of the models local transform variables (bi)are updated after the ASM shape is found • If the ASM shape (u) is not defined within the limits of the PDM shape space the local transform variables (bi)are rescaled appropriately • Shape information may be lost • Re-active solution

23. Unified Cost Function • Steps in shape deformation where ASM shape not within PDM shape space limits

24. Unified Cost Function • Proposed solution implemented by an unified cost function • Pro-active solution • Efficiently solved as an quadratic programming problem

25. Unified Cost Function • The cost function can be viewed as, • vT = (3nx1) vector global transformed mean shape • DT-1 = (3nx3n) matrix global transformed inverse covariance matrix • u = (3nx1) vector initial PDM mean shape or previous ASM shape • N = (3nxn) matrix the normal vectors • * = (nx1) vector profile locations of the most stable edges •  = (nx1) vector most optimal profile locations

26. Overview • Abstract • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments & Results • Conclusion

27. Experiments & Results • Developed using ITK and VXL C++ open source libraries • Subset of 10 high resolution MRI brain images from pediatric study • 256x256x192 resolution • Inter-voxel spacing 1.0mm

28. Experiments & Results • Left hippocampus PDM • 42 shape instances • 642 corresponded landmark points • Corresponded using MDL • Left caudate nucleus PDM • 85 shape instances • 742 corresponded landmark points • Corresponded using SPHARM

29. Experiments & Results • Each PDM mean shape was manually initialized using Insight-SNAP • Convergence was achieved when either the global transform variables or mahalanobis distance between ASM shape and PDM mean shape were at an minimum. • Convergence was typically achieved between 5 to 7 ASM iterations using +/- 4 (k=9) profile locations along each landmark points normal vector

30. Experiments & Results

31. Experiments & Results

32. Experiments & Results • ASM segmented performance was compared against Atlas-based method • Performance was evaluated using the following measures: • Pearson correlation coefficient: volumetric correlation • Dice coefficient: volumetric overlap

33. Experiments & Results

34. Overview • Abstract • Motivation • General ASM Algorithm • Robust Edge Detection • Unified Cost Function • Experiments / Results • Conclusion

35. Conclusion • Presented two new strategies to address limitations of current ASM. • Robust edge detection to reduce likelihood of spurious edge • Pro-active solution ensure ASM approximated shape is defined within PDM shape space limits using unified cost function • Additional research is required to address the sensitivity of the initial placement • Implement fully-automatic method