1. 12-Apr-20071 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. 12-Apr-20072 Overview Introduction Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments / Results Conclusion

3. 12-Apr-20073 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. 12-Apr-20074 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. 12-Apr-20075 Overview Introduction Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments / Results Conclusion

6. 12-Apr-20076 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. 12-Apr-20077 Motivation http://www.emedicine.com/radio/topic443.htm#target2

8. 12-Apr-20078 Motivation http://www.sci.uidaho.edu/med532/basal.htm

9. 12-Apr-20079 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. 12-Apr-200710 Overview Introduction Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments / Results Conclusion

11. 12-Apr-200711 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. 12-Apr-200712 Overview Introduction Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments / Results Conclusion

13. 12-Apr-200713 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. 12-Apr-200714 Robust Edge Detection Example sagittal plane edges for hippocampus Image slice Gradient magnitude slice

15. 12-Apr-200715 Robust Edge Detection Example coronal plane edges for hippocampus Image slice Gradient magnitude slice

16. 12-Apr-200716 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. 12-Apr-200717 Robust Edge Detection Additionally, each landmark points normal vector is determined by the surface mesh 1 2 4 3 5 6 8 7 A E C D B F G n 6 = ¼ x (n D + n E + n F + n G ) = n 6 / || n 6 ||

18. 12-Apr-200718 Robust Edge Detection

19. 12-Apr-200719 Robust Edge Detection Generally, edges detection along search paths are considered dangerous Subject to noise Spurious (false) edges

20. 12-Apr-200720 Robust Edge Detection Propose an new neighborhood solution Spatially consistent profile location (  i  ) Reduces the likelihood of an false edge

21. 12-Apr-200721 Overview Introduction Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments / Results Conclusion

22. 12-Apr-200722 Unified Cost Function Traditionally each of the models local transform variables (b i ) 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 (b i ) are rescaled appropriately Shape information may be lost Re-active solution

23. 12-Apr-200723 Unified Cost Function Steps in shape deformation where ASM shape not within PDM shape space limits

24. 12-Apr-200724 Unified Cost Function Proposed solution implemented by an unified cost function Pro-active solution Efficiently solved as an quadratic programming problem

25. 12-Apr-200725 Unified Cost Function The cost function can be viewed as, v T = (3nx1) vector global transformed mean shape D T -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. 12-Apr-200726 Overview Abstract Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments & Results Conclusion

27. 12-Apr-200727 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. 12-Apr-200728 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. 12-Apr-200729 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. 12-Apr-200730 Experiments & Results

31. 12-Apr-200731 Experiments & Results

32. 12-Apr-200732 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. 12-Apr-200733 Experiments & Results

34. 12-Apr-200734 Overview Abstract Motivation General ASM Algorithm Robust Edge Detection Unified Cost Function Experiments / Results Conclusion

35. 12-Apr-200735 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