Tooth Segmentation on Dental Meshes Using Morphologic Skeleton

1. Computers & Graphics CAD/Graphics 2013, Hong Kong Tooth Segmentation on Dental Meshes Using Morphologic Skeleton M.Eng. Kan WU Li CHEN Ph.D. School of Software Tsinghua University, P. R. of China Ph.D. Ph.D. Jing LI Yanheng ZHOU Department of Orthodontics Peking University School and Hospital of Stomatology, P. R. of China

2. Background our work

3. Contribution • an applicable pipeline for dental mesh segmentation • avoid complex mesh feature estimation • significantly reduced user interaction • experiments on various clinical cases of different tooth • shapes and various levels of crowding problems

4. Problems of Current Work Kumar et al. 2011 • not sufficiently accurate • affected by feature disturbance Kronfeld et al. 2010 • intensive interaction “3Shape”

5. A Good Dental Segmentation Approach Should • locate teeth area automatically • separate adjacent teeth automatically morphologic skeleton • less dependent on • complex feature estimation • smoothed & fitted boundary

6. Why Morphologic Skeleton • insensitive to feature missing & disturbance ACCURACY • simplified approximation of mesh features EFFICIENCY • easy separation of adjacent objects REDUCED INTERACTION

7. Dental Mesh Segmentation Pipeline

8. 1st Step: Locating Teeth Parts automatic plane cutting region-growing original mesh skeletonization

9. 1st Step: Locating Teeth Parts – (1)Estimating Cutting Plane PCA-based plane initialization energy field

10. (1)Estimating Cutting Plane – PCA-based Plane Initialization barycentric point eigenvector corresponding to the smallest eigenvalue set of feature vertices Kronfeld et al., 2010

11. (1)Estimating Cutting Plane – Energy Field weighted distance feature points connected to v

12. 1st Step: Locating Teeth Parts – (2)Morphologic Skeletonization skeleton curvature threshholding connectivity filtering morphologic operation skeletonization

13. 1st Step: Locating Teeth Parts – (2)Morphologic Skeletonization original morphologic skeleton (Rossl et al., 2000) improved morphologic skeleton

14. 1st Step: Locating Teeth Parts – (3)Region-Growing seed points skeleton

15. 2nd Step: Separating Teeth cut

16. 2nd Step: Separating Teeth – Various Scenarios discarded cut valid cut

17. 2nd Step: Separating Teeth – Results

18. 3rd Step: Smoothing Tooth Contours 3D contours interpolated 3D contours 2D contours sampled 2D contours sampled 3D contours

19. 3rd Step: Smoothing Tooth Contours – 2D Sampling Length Change Measure Direction Change Measure middle point center point for contour

20. 3rd Step: Smoothing Tooth Contours – 2D Sampling Length Measures Direction Change Measures sign(x) = 1 if x > 0, otherwise -1

21. Results – Mild Tooth Crowding skeletonization & region-growing separating & contour smoothing cutting plane estimation original model

22. Results – Moderate Tooth Crowding skeletonization & region-growing separating & contour smoothing cutting plane estimation original model

23. Results – Severe Tooth Crowding skeletonization & region-growing separating & contour smoothing cutting plane estimation original model

24. Results

25. Results

26. Results

27. Comparative Results – Published Approaches Kronfeld et al. 2010 our approach Kumar et al. 2011 our approach

28. Comparative Results – “3Shape” Software “3Shape” Software our approach “3Shape” Software our approach when user interaction is not sufficiently accurate enough

29. Accuracy Evaluation – Mean Errors The mean errors that compare our results to manually labeled ground truth. The unit is mm.

30. Accuracy Evaluation – Error Distribution the distribution of particular error values across all segmented boundary vertices. The blue, yellow, red lines indicate the ranges of [0, 0.25], [0.25, 0.5], [0.5, 1.5], respectively.

31. User Interaction Evaluation Time consumed by user interactions. The blue and yellow lines indicate manual boundary completion and additional seed adding, respectively. The unit is s

32. Limitations user interaction still needed no GPU accelerating Future Work a dental mesh benchmark GPU accelerating completely eliminate user interaction

33. Demo

34. THANK YOU Li CHEN (chenlee@mail.tsinghua.edu.cn) Kan WU (ulmonkey1987@gmail.com) Jing LI (lijing1101@gmail.com) Yanheng ZHOU (yanhengzhou@gmail.com)