Automated Breast Density Measurement

Breast density was measured by using fully automated software. Absolute dense area and area percent density (PD %) were estimated by using a publically available software tool [31 ], the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA), based on our previously proposed adaptive multi-cluster fuzzy c-means segmentation algorithm [32 (link)]. The LIBRA algorithm has been previously validated against the current standard semi-automated Cumulus method [33 (link)], showing similar agreement for both raw (i.e., “For Processing”) and vendor post-processed (i.e., “For Presentation”) digital mammograms (Fig. 1) [32 (link)], for the same vendor used in this study. Briefly, the algorithm first applies an edge-detection algorithm to delineate the boundary of the breast and the pectoral muscle. An adaptive multi-class fuzzy c-means algorithm is applied to identify and partition the image gray levels (Fig. 1b) within the mammographic breast tissue area, B_A, into regions (i.e., clusters) of similar x-ray attenuation (Fig. 1c). These clusters are then aggregated by a support-vector machine classifier to a final absolute dense area, D_A, segmentation (Fig. 1d). The ratio of the absolute dense area to the total breast area is used to obtain a measure of breast percent density (PD %): $\mathrm{P}\mathrm{D}\mathrm{\%}=\frac{D_A}{B_A}$
Fig. 1

Example of density segmentation using the LIBRA software tool. a Left mediolateral oblique “For Processing” raw mammogram of a 57-year-old woman with a negative screening exam. b Breast image intensity histogram with fuzzy c-means clustering centroids (vertical lines). c Intensity-clustered breast image. d The final breast and dense tissue segmentation. LIBRA Laboratory for Individualized Breast Radiodensity Assessment

Absolute dense tissue volume and volume percent density (VD %) were automatically assessed by using FDA-cleared software (Quantra™ version 2.0; Hologic Inc., Bedford, MA, USA). The algorithm is based on the widely validated method of Highnam et al. [34 (link)] adapted for digital mammography [35 ]. Briefly, this method quantifies the thickness of dense (i.e., fibroglandular) tissue within each image pixel based on physical parameters of the breast and the imaging system as well as on imaging physics of individual exposures, such as attenuation coefficients for breast tissue, x-ray spectra for the target material, x-ray energy (i.e., peak kilovoltage), exposure, and organ dose (i.e., decigray). Aggregation of the per-pixel volumes for the entire breast allows estimation of the total breast volume, B_V, and dense tissue volume, D_V. The ratio of absolute dense tissue volume to absolute breast volume provides a measure of VD % as: $\mathrm{V}\mathrm{D}\mathrm{\%}=\frac{D_V}{B_V}$
Lastly, for comparison with the automated density measures, we also obtained standard four-category BI-RADS density estimates via retrospective review of archived clinical reports, in which the density assessment was made at the time of routine clinical evaluation by the interpreting breast radiologist for that individual mammography study.