BGNet: Boundary‐guided network for polyp segmentation

Abstract

Colonoscopy is one of the most direct and effective methods for detecting colon polyps; they are crucial for early screening and prevention of colorectal cancer (CRC). Accurate segmentation of polyp images is significant for the clinical management and treatment of CRC. However, polyp image segmentation is a challenging task because polyps vary in size, shape, and color, and have low contrast with the surrounding tissue and mucosa. To address these challenges, we propose a novel network called BGNet for polyp segmentation. BGNet consists of three modules: a boundary feature extraction module (BFEM), a mutual optimization module (MOM), and a convolutional attention module (CBAM). The BFEM extracts boundary features and predicts boundary maps, which complement and guide region features to accurately predict polyp regions and help BGNet generate precise prediction masks. The MOM optimizes the extracted features to enhance fine‐grained feature representation. The CBAM performs attention operations on the feature map to focus on informative regions and suppress irrelevant information. We conduct quantitative and qualitative evaluations on five benchmark datasets, and the results show that BGNet outperforms other methods and has strong learning ability and generalization ability.