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

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.

Keywords: 3D image; Breast surgery; Classification; Gaussian curvature; Histogram matching, Breast ptosis; Orientation; Stereophotogrammetry.

Fig. 1

Visual classification of breast ptosis in lateral (top) and frontal (bottom) views. The red cross indicates the nipple, pink indicates the level at which the IMF is visualized, and the cyan indicates the lowest visible point. Grade 0 (no ptosis) is defined as a breast that has the nipple and parenchyma (glandular tissue and fat which compose the breast) sitting above the IMF. In grade 1 (minor ptosis), the nipple lies at the level of the IMF and above the lowest contour of the breast. In grade 2 (moderate ptosis), the nipple lies below the level of the IMF but remains above the lowest contour of the breast, and in grade 3 (major ptosis), the nipple lies well below the IMF and close to or at the lower contour of the breast. Note that the IMF is occluded in the frontal view for moderate and major ptosis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Flowchart for ptosis assessment from 3D surface images.

Fig. 3

Selection of the region of interest (ROI). (A) Image with both breasts. (B) Cropped ROI for the right breast. (C) Cropped ROI for the left breast. (D) ROI for the left breast with four quadrants labeled a, b, c, and d.

Fig. 4

Representative Gaussian curvature maps of breasts from all four ptosis grades. The top panel shows the front view of the breast, while the bottom panel shows the lateral view. The curvature values are mapped so that nearly planar or cylindrical regions with values close to 0 are green, while positive values ranging from yellow to red represent convexity, and negative values ranging from cyan to dark blue represent concavity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Calculation of surface normal from the triangular surface mesh. (A) A portion of the surface mesh of a breast. (B) A single triangle from the surface mesh showing the surface normal vector n and vectors a and b, representing the two sides of the triangle. (C) The computed surface normals (shown as blue arrows) are superimposed on the curvature mapped image. The direction of the arrows denotes the orientation of the surface. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Representative histogram templates for the four ptosis grades generated from concatenated histograms of quadrants a, b, c and d for (A) surface curvature and (B) orientation. (For improved visualization of the color line graphs of the different ptosis grades in this figure, the reader is referred to the web version of this article.)

Fig. 7

Comparison of 2D and 3D features for rating breast ptosis. Kim1 and Kim 2, measures of Kim et al.; LH, LaTrenta and Hoffman classification; G, Gaussian; O, orientation.

Fig. 8

Comparison of classification accuracy of 3D and 2D features for ptosis grades. Kim1 and Kim 2, measures of Kim et al.; LH, LaTrenta and Hoffman classification; G, Gaussian; O, orientation.