Boundary-Supplementary Network for Carotid Plaque Segmentation in Ultrasound Images

Accurate measurement of carotid plaques in ultrasound images is essential for the early prevention and intervention of stroke. However, carotid plaques exhibit complex and diverse shapes, with blurry and irregular boundaries, making this task very challenging. This paper proposes a novel Boundary-Supplementary Network (BSNet) for carotid plaque segmentation. BSNet follows an encoder-decoder structure and incorporates two essential modules: the Multi-Scale Attention Module (MSAM) and the Boundary-Supplementary Module (BSM). MSAM is deployed at each layer to comprehensively analyze the feature at different spatial scales and dynamically fuse multi-scale features using the attention mechanism. It aids the network in better recognizing carotid plaques with irregular shapes. BSM incorporates the outputs of the MSAM at the current and adjacent high layer with the prediction map of the adjacent high layer to enhance boundary regions. Through topdown deep supervision, BSNet can accurately localize the carotid plaque regions with clear boundaries. Extensive experiments on a collected dataset demonstrate that BSNet outperforms 8 state of-the-art methods in carotid plaque segmentation.