Polyp segmentation algorithm combining boundary knowledge and dual-branch attention

Aiming at the problem of poor precision of polyp segmentation due to unclear boundaries between polyps and the surrounding mucosa, a polyp segmentation model (BDANet) combining boundary knowledge and dual-branch attention is proposed for accurate polyp segmentation in colonoscopy images. A boundary extraction and awareness module is designed to simultaneously utilize low-level detail information and high-level semantic information for generating edge detail features. These features are fused with global features to generate globally aware information with boundary perception. A residual learning structure is adopted to emphasize boundary learning at different levels. Side output features from various levels are integrated from deep to shallow. A dual-branch attention module is designed to learn boundaries of target objects both in forward and reverse directions, reducing the uncertainty of model in predicting boundary regions. Quantitative and qualitative evaluations on 7 metrics of 4 commonly used polyp segmentation datasets demonstrate that the proposed BDANet can effectively improve segmentation accuracy.