Mass segmentation in automated breast ultrasound using an enhanced attentive UNet

Abstract

Breast cancer is the most diagnosed cancer among women. Early detection of breast cancer through screening high risk population significantly reduces the mortality rate. To this end, 3-D Automated Breast Ultrasound (ABUS) is a favorable modality due to its complementary information in comparison with other modalities. Different characteristics of breast masses and the large number of ABUS slices result in time consuming and exhaustive evaluations by radiologists. This study aims to develop a fully-automatic segmentation system that acts as a second interpreter to facilitate the interpretation process. Specifically, a bidirectional convolutional long short-term memory (Bi-ConvLSTM) network is adopted for tumor detection. Afterwards, a 2-D attentive UNet takes an ABUS slice and its approximate saliency map to precisely segment breast masses. The attention blocks of the proposed network use this map to direct the network’s attention to regions with high saliency values. We proposed two enhanced versions of the base attention module with superior performance and less complexity. To increase the robustness against the mass size variations, we stacked several side outputs from different stages of the network and control their contribution to final results by a Convolutional Block Attention Module (CBAM). The effectiveness of the proposed modifications is verified with step-by-step evaluations, and our optimal model achieves a Dice similarity index of 85.82% on a dataset of 60 volumes.