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Proceedings Paper

Spherical harmonics for modeling shape transformations of breasts following breast surgery
Author(s): U. Sampathkumar; Z. Nowroozilarki; G. P. Reece; S. E. Hanson; F. A. Merchant
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Paper Abstract

Breast shape aesthetic is most desired outcome of cosmetic and reconstructive breast surgery. Post-operative (post-op) patient satisfaction largely depends on patient expectations, hence appropriately communicating information about surgical options to patients and moderating patient expectations is critical in surgical planning. Breast modeling and computational simulation can help mitigate this challenge and provide an effective tool for presenting potential surgical outcomes and eliciting patient preferences. Most available computational models lack the ability to provide realistic estimation of breast shape changes. We have previously developed a Fourier spherical harmonics (SPHARM) based computational approach to model breast shape1. SPHARM modeling results in 1320 coefficients that are effective descriptors of the 3D breast shape. In this study, we develop a framework to transform the SPHARM coefficients of the pre-operative (pre-op) breast to generate an estimation of the post-op breast shape for cosmetic (e.g. implant-based augmentation) and reconstructive (e.g. implant, autologous tissue, etc.) surgery procedures. Least squares optimization was used to realize the transformation between the pre- and post-op SPHARM coefficients. We demonstrate the feasibility of our approach using data from patients who have undergone bilateral implant-based breast reconstruction as part of cancer treatment. We trained a random forest regression2 function using SPHARM coefficients and their corresponding shape transformation vectors for 21 preop breasts and validated the regressor using a test dataset of 41 breasts. Our preliminary results demonstrate feasibility of the proposed data-driven approach to model transformation of the pre-op breast to its post-op form for a given surgical procedure.

Paper Details

Date Published: 16 March 2020
PDF: 9 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113152H (16 March 2020); doi: 10.1117/12.2549776
Show Author Affiliations
U. Sampathkumar, Univ. of Houston (United States)
Z. Nowroozilarki, Univ. of Houston (United States)
G. P. Reece, MD Anderson Cancer Ctr. (United States)
S. E. Hanson, MD Anderson Cancer Ctr. (United States)
F. A. Merchant, Univ. of Houston (United States)
MD Anderson Cancer Ctr. (United States)

Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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