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[Defense] Statistical Shape Modeling and Post-Operative Shape Estimation in Reconstructive Breast Surgeries

Friday, December 9, 2022

2:00 pm - 3:00 pm

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Urmila Sampathkumar
will defend her proposal
Statistical Shape Modeling and Post-operative Shape Estimation in Reconstructive Breast Surgeries


Abstract

Simulation and prediction tools for communicating postoperative breast shape changes and for elicitation of patient preferences following reconstructive surgery are limited. We propose a shape simulation method on 3D surface images that allows data-driven deformable, non-rigid localized changes to breast shape. Active Shape Models (ASM) is a statistical approach, that leverages specificity (common features) and variability (features of significant variations in the given dataset) for shape modeling. We previously developed a spherical harmonics-based breast shape model (SPHARM). In this work we utilize the descriptors from SPHARM modeling with ASM to achieve breast shape simulations. The ASM shape parameters are then mapped to semantically and clinically relevant breast features (such as breast height, width, projection, ptosis) through fitting multi variate regression. To obtain a prediction of post-operative breast shape, we utilize data fusion using Kalman Filtering. The ability to preoperatively obtain an estimate of postoperative shape and create desired breast shape simulations will help plastic surgeons in surgical planning and patient expectation management.


Friday, December 9, 2022
2:00PM - 3:00PM CT

Online via Zoom

Dr. Fatima Merchant, dissertation advisor

Faculty, students, and the general public are invited.

Doctoral Proposal Defense - Thumbnail 2