Automated Segmentation of Breast Arterial Calcifications from Digital Mammography
Kaier Wang, Nabeel Younus Khan, Ralph P. Highnam · 2019
Breast arterial calcifications (BACs) are formed when calcium is deposited in the walls of arteries in the breast. The accurate segmentation of BACs is a critical step for risk assessment of cardiovascular disease from a mammogram. This paper evaluates the performance of three deep learning architectures, YOLO, Unet and DeepLabv3+, on detecting BACs in digital mammography. In comparison, a simple Hessian-based multiscale filter is developed to enhance BACs pattern, then a self-adaptive thresholding algorithm is applied to obtain the binary mask of BACs. As BACs are relatively small in size, we developed a new metric to better evaluate the small object segmentation. In this study, 135 digital mammographic images containing labelled BACs were obtained, in which 80% for training deep learning networks and 20% for validation. The results show that our Hessian-based filtering method achieves a highest accuracy on validation data, and DeepLabv3+ falls behind with little effectiveness. We conclude simple filtering technique is effective in BACs extraction, and DeepLabv3+ is an expensive alternative in terms of its computational cost and configuration complexity.