A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound
  Semantic Segmentation
Kyle Lucke Aleksandar Vakanski Min Xian
In recent years, convolutional neural networks for semantic segmentation of
breast ultrasound (BUS) images have shown great success; however, two major
challenges still exist. 1) Most current approaches inherently lack the ability
to utilize tissue anatomy, resulting in misclassified image regions. 2) They
struggle to produce accurate boundaries due to the repeated down-sampling
operations. To address these issues, we propose a novel breast anatomy-aware
network for capturing fine image details and a new smoothness term that encodes
breast anatomy. It incorporates context information across multiple spatial
scales to generate more accurate semantic boundaries. Extensive experiments are
conducted to compare the proposed method and eight state-of-the-art approaches
using a BUS dataset with 325 images. The results demonstrate the proposed
method significantly improves the segmentation of the muscle, mammary, and
tumor classes and produces more accurate fine details of tissue boundaries.