Spherical harmonics for modeling shape transformations of breasts following breast surgery

U. Sampathkumar, Z. Nowroozilarki, G. P. Reece, S. E. Hanson, F. A. Merchant

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
ISBN (Electronic)9781510633971
StatePublished - 2020
EventMedical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceMedical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Country/TerritoryUnited States


  • Breast shape transformation modeling
  • Random forest regression
  • Spherical harmonics

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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