3D Analysis of Breast Changes for Medical Images

1. 3D Analysis of Breast Changes for Medical Images Lijuan Zhao Advisors: Prof. Fatima Merchant Prof. Shishir Shah

2. OUTLINE • Motivation • Computational Problem • Challenges • Literature Review • Future Work

3. Motivation • Breast Reconstruction • Breast cancer is the most life-threatening disease in women • Breast cancer treatments usually lead to complete or partial breast removal • Breast reconstruction can help breast cancer survivors regain their quality of life

4. Motivation (cont’d) • Measurements of breast aesthetics • Volume, symmetry, ptosis, projection, etc • Limitations: only estimate surgical results unable to give guidance for surgery • Analysis of change for each point on breast • Better evaluation of surgical outcomes • Provide guidance for surgery

5. Computational Problem • Example of 3D torso image 2D texture image mapped onto surface Triangular mesh surface Point cloud

6. Computational Problem (cont’d) • Retrieve breast data from 3D torso images • Analyze breast changes for different visits for same patient Visit 1 Visit 2 Visit 3

7. Challenges • Chest walls are not matched for different visits • Coordinate systems may not be same • Patient weight change • 3D corresponding are required

8. Challenges (cont’d) • Manually retrieve data may change points coordinates • The transformations of the breast data are non-rigid

9. Literature Review (1) Robust point set registration using Gaussian mixture models • Using Gaussian mixture models to represent point sets • Divergence measure: L2 distance • Deformation model: thin-plate splines (TPS)+ gaussian radial basis functions (GRBF) • Cost function: • PROS: efficient and robust • CONS: only works for pair-wise point set

10. Literature Review (cont’d) (2) Group-wise point-set registration using a novel CDF-based Havrda-Charvat divergence • Using Dirac mixture models to represent point sets • Divergence measure: CDF-HC divergence • Deformation model: thin-plate splines (TPS) • Cost function: • PROS: efficient and simple to implement; works for group-wise point sets • CONS: not robust for noise and outliers

11. Future Work • Step 1: chest wall calibration • Choose some fiducial points and connect them • Choose same points on different images • Construct the mathematical model

12. Future Work (cont’d) • Step 2: automatically retrieve the breast data • Based on mathematical model, calculate the corresponding coordinates for points on chest wall • Using curvature property retrieve the breast data

13. Future Work (cont’d) • Step 3: using 3D group-wise point sets non-rigid registration to analyze breast changes. - Down sampling point cloud (if necessary) • VTK - Propose new method with good cost function and optimization scheme • suitable model to represent point sets • divergence measure • deformable model