A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation
This work addresses the problem of accurate tissue boundary segmentation in breast ultrasound images for medical diagnosis, representing an incremental improvement over existing methods.
The paper tackled the challenges of misclassified regions and inaccurate boundaries in breast ultrasound semantic segmentation by proposing an anatomy-aware network with a smoothness term, resulting in significant improvements in segmentation accuracy for muscle, mammary, and tumor classes on a dataset of 325 images.
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.