Computation of breast ptosis from 3D surface scans of the female torso. (1st November 2016)

• Record Type: Journal Article
• Authors: Li, Danni; Cheong, Audrey; Reece, Gregory P.; Crosby, Melissa A.; Fingeret, Michelle C.; Merchant, Fatima A.
• Is Part Of: Computers in biology and medicine. Volume 78(2016)
• Page Start: 18
• Page End: 28
• Publication Date: 2016-11-01
• Subjects: 3D image -- Stereophotogrammetry -- Breast surgery -- Gaussian curvature -- Orientation -- Histogram matching, Breast ptosis -- Classification; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.09.002
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Graphical abstract: Highlights: New approach proposed for measuring breast ptosis from 3D torso scans. Use of curvature and orientation for rating breast ptosis from 3D scans of torso. 3D features yielded better performance for rating breast ptosis than 2D features. 3D features eliminate fiducial point identification thereby mitigating user bias.