Longitudinal Characterization of Breast Morphology during Reconstructive Surgery

1. Longitudinal Characterization of Breast Morphology during Reconstructive Surgery Lijuan Zhao Advisors: Prof. Fatima Merchant Prof. Shishir Shah

2. Motivation • Breast Reconstruction • Breast cancer treatments usually lead to complete or partial breast removal • Breast reconstruction surgery is used to rebuild lost or deformed tissue • Surgery is completed in a multi-step process lasting for 2-3 years • Currently there is no process to monitor or quantify changes occurring in breast morphology through the reconstruction process • This information is needed to better assess surgical outcome

3. Current Quantitative Parameters for Assessment of Breast Reconstruction • Measurements of breast aesthetics • Volume, symmetry, proportion, projection, ptosis, etc. • Current parameters provide a global assessment at a given time point • No measure are available to correlate local morphological changes over time (a) (b) (c) (d) Volume Symmetry Proportion Projection Ptosis (e)

4. Computational Challenge • Retrieve breast data from 3D torso images • Analyze breast changes for different visits for same patient Visit 1 Visit 2 Visit 3

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

6. Flow-chart of processing steps

7. Spatio-Temporal Correspondence • Chest walls are not matched for different visits • Coordinate systems may not be same • Patient weight changes may occur • 3D correspondence is required

8. Mathematical Model for Spatio-temporal Correspondence • Construct corresponding model • Choose some fiducial points • Connect them to form a geometry • Choose same points on different images • Construct the correspondence between torsos • Experimental measurements: • Which fiducial points are suitable • Linear or non-linear relationships for distances between fiducial points in different images

9. Quantitative correlation of changes • The transformations of breast data are deformable • Using 3D group-wise point sets based non-rigid registration to analyze breast changes • Propose new method with good cost function and optimization scheme • Develop appropriate model to represent local topology of point sets • Develop Similarity Metric for registration of temporal data sets • Develop methods to quantify changes in breast morphology over time

10. Thank You! Questions?

11. Possible Approaches • 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) • PROS: efficient and robust • CONS: but only works for pair-wise point set • Group-wise point-set registration using a novel CDF-based Havrda-Charvatdivergence • Using Dirac mixture models to represent point sets • Divergence measure: CDF-HC divergence • Deformation model: thin-plate splines (TPS) • PROS: efficient and simple to implement • CONS: not robust for noise and outliers