Approaches to Automatic Classification, Detection and Segmentation of Breast Arterial Calcification Using Deep Learning

Objective

Cardiovascular disease (CVD) is the leading cause of premature death in the United Kingdom with one type, coronary artery disease, killing more than two times as many women as breast cancer. Recently, researchers have noted that breast arterial calcification (BAC), which is regularly observed as an incidental finding on mammograms, could be used to risk-stratify women for CVD. However, identifying BAC is known to be a tedious, expensive and time-consuming process. Thus, this paper investigates deep learning models for BAC classification, object detection and segmentation.

Methodology

A data set, annotated under the guidance of two consultant radiologists, was created using data augmentation. This was used to evaluate several alternative deep learning models.

Results

A modified ResNet22 classification network achieved a test accuracy of 80%, indicating that this method could be used as a flag for the presence or absence of BAC. We also used this network for feature extraction in a YOLOv4 BAC object detection network. Despite improving on a recent similar study, this latter network performed poorly with very low average precision scores at several thresholds. More promising was our DeepLabv3+-based BAC segmentation network, which reached similar high global accuracy scores to three recent studies and a BFScore of over 70% specifically for BAC. It also performed satisfactorily on an unseen data set.

Conclusions

These results show the potential for using classification and segmentation models as part of a pipeline for detecting BAC.