MCG&BA‐Net: Retinal vessel segmentation using multiscale context gating and breakpoint attention

Abstract

Abstract The accurate segmentation of blood vessels plays a crucial role in screening, diagnosis and treatment of multiple diseases. However, current automated segmentation approaches do not pay enough attention to the vascular topology errors (such as mistaking vessel‐breakpoints), resulting in considerable scattered vessel‐fragments in segmentation results. This article proposes a retinal vessel segmentation model using multi‐scale context gating and breakpoint attention mechanism, called MCG&BA‐Net. Specifically, it obtains a feature map containing contextual information of vessels through an introduced multi‐scale context module, and then filters the redundant features and noises by a gated structure to highlight target features. Furthermore, a kind of breakpoint attention module is proposed, which can locate and focus on potential breakpoint areas, thereby facilitating accurate segmentation results of tree‐like fine vessels. Extensive confirmatory and comparative experiments have been conducted on five public datasets, including three benchmark datasets, that is, DRIVE, CHASDB1 and SATRE, and two clinical datasets, that is, fundusimage1000 and RFMID. The AUC scores on the benchmark datasets are 0.9878, 0.9923 and 0.9942, respectively. Among them, the AUC score on CHADEDB1 and STARE outperforms the state‐of‐the‐art results. In addition, experimental results on the two clinical datasets demonstrate strong generalization capability of the propose method, indicating high clinical application values.