MBFF-Net: Multi-Branch Feature Fusion Network for Carotid Plaque Segmentation in Ultrasound

Stroke is one of the leading causes of death around the world. Segmenting atherosclerotic plaques in carotid arteries from ultrasound images is of great value for preventing and treating ischemic stroke, yet still challenging due to the ambiguous boundary of plaque and intense noise in ultrasound. In this paper, we introduce a new approach for carotid plaque segmentation, namely Multi-Branch Feature Fusion Network (MBFF-Net). Inspired by the prior knowledge that carotid plaques generally grow in carotid artery walls (CAWs), we design a Multi-Branch Feature Fusion (MBFF) module with three branches. Specifically, the first two branches are well-designed to extract plaque features of multiple scales and different contexts, and the other branch is to exploit the prior information of CAWs. In addition, a boundary preserving structure is applied to alleviate the ambiguity of plaque boundary. With the proposed MBFF and the novel structure, our model is capable of extracting discriminative features of plaques and integrating the location information of CAWs for better segmentation. Experiments on the clinical dataset demonstrate that our model outperforms state-of-the-art methods. Code is available at https://github.com/mishiyu/MBFF.