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

Comput Biol Med

Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, USA; Department of Engineering Technology, University of Houston, Houston, TX 77204, USA. Electronic address:

Published: November 2016

AI Article Synopsis

  • Stereophotography is being used in clinical breast surgery to measure breast morphology through 3D surface images.
  • A new algorithm assesses breast ptosis by analyzing 3D features like surface curvature and orientation.
  • This 3D method showed about 13% higher accuracy than 2D methods and significantly improved precision, recall, and F-score, offering a more objective approach to rating breast ptosis.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5077640PMC
http://dx.doi.org/10.1016/j.compbiomed.2016.09.002DOI Listing

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