Computation of breast ptosis from 3D surface scans of the female torso

Danni Li, Audrey Cheong, Gregory P. Reece, Melissa A. Crosby, Michelle C. Fingeret, Fatima A. Merchant

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Stereophotography is now finding a niche in clinical breast surgery, and several methods for quantitatively measuring breast morphology from 3D surface images have been developed. Breast ptosis (sagging of the breast), which refers to the extent by which the nipple is lower than the inframammary fold (the contour along which the inferior part of the breast attaches to the chest wall), is an important morphological parameter that is frequently used for assessing the outcome of breast surgery. This study presents a novel algorithm that utilizes three-dimensional (3D) features such as surface curvature and orientation for the assessment of breast ptosis from 3D scans of the female torso. The performance of the computational approach proposed was compared against the consensus of manual ptosis ratings by nine plastic surgeons, and that of current 2D photogrammetric methods. Compared to the 2D methods, the average accuracy for 3D features was ~13% higher, with an increase in precision, recall, and F-score of 37%, 29%, and 33%, respectively. The computational approach proposed provides an improved and unbiased objective method for rating ptosis when compared to qualitative visualization by observers, and distance based 2D photogrammetry approaches.

Original languageEnglish (US)
Pages (from-to)18-28
Number of pages11
JournalComputers in Biology and Medicine
Volume78
DOIs
StatePublished - Nov 1 2016

Keywords

  • 3D image
  • Breast surgery
  • Classification
  • Gaussian curvature
  • Histogram matching, Breast ptosis
  • Orientation
  • Stereophotogrammetry

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

MD Anderson CCSG core facilities

  • Clinical Trials Office

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