SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms

Abstract

Purpose: Measurements of breast arterial calcifications (BAC) can offer a personalized, noninvasive approach to risk-stratify women for cardiovascular disease such as heart attack and stroke. We aim to detect and segment breast arterial calcifications in mammograms accurately and suggest novel measurements to quantify detected BAC for future clinical applications. Methods: To separate BAC in mammograms, we propose a light-weight fine vessel segmentation method Simple Context U-Net (SCU-Net). Due to the large image size of mammograms, we adopt a patch-based way to train SCU-Net and obtain the final whole-image-size results by stitching patch-wise results together. To further quantify calcifications, we test five quantitative metrics to inspect the progression of BAC for subjects: Sum of Mask Probability Metric (PM), Sum of Mask Area Metric (AM), Sum of Mask Intensity Metric (SIM), Sum of Mask Area with Threshold Intensity Metric (TAM_X) and Sum of Mask Intensity with Threshold X Metric (TSIM_X). Finally, we demonstrate the ability of the metrics to longitudinally measure calcifications in a group of 26 subjects and evaluate our quantification metrics compared to calcified voxels and calcium mass on breast CT for 10 subjects. Results: Our segmentation results are compared with state-of-the-art network architectures based on recall, precision, accuracy, F1-score/Dice Score and Jaccard Index evaluation metrics and achieve corresponding values of 0.789, 0.708, 0.997, 0.729, and 0.581 for whole-image-size results. The quantification results all show >95% correlation between quantification measures on predicted masks of SCU-Net as compared to the groundtruth and measurement of calcification on breast CT. For the calcifications quantification measurement, our calcification volume (voxels) results yield R2-correlation values of 0.834, 0.843, 0.832, 0.798, and 0.800 for the PM,AM,SIM , TAM_100, TSIM_100 metrics, respectively; our calcium mass results yield comparable R2-correlation values of 0.866, 0.873, 0.840, 0.774, and 0.798 for the same metrics. Conclusions: SCU-Net is a simple method to accurately segment arterial calcification retrospectively on routine mammograms. Quantification of the calcifications based on this segmentation in the retrospective cohort study has sufficient sensitivity to detect the normal progression over time and should be useful for future research and clinical applications.