The Devil Is in the Boundary: Boundary-Enhanced Polyp Segmentation

Due to the various appearance of the polyps and the tiny contrast between the polyp area and its surrounding background, accurate polyp segmentation has become a challenging task. To tackle this issue, we introduce a boundary-enhanced framework for polyp segmentation, called the Focused on Boundary Segmentation (FoBS) framework, that leverages multi-level collaboration among sample, feature, and optimization. It places greater emphasis on the polyp boundary to improve the accuracy of segmentation. Firstly, a boundary-aware mixup method is designed to improve the model’s awareness of the boundary. More importantly, we propose deformable laplacian-based feature refining to explicitly strengthen the representation ability of the boundary features. It employs a deformable Laplacian refinement function to capture discriminative information from a deformable perceptual field, thereby improving its ability to adapt to boundary variations. In addition, we introduce the self-adjusting refinement coefficient learning that enables adaptive control over the refinement strength at each location. Furthermore, we develop a location-sensitive compensation criterion that assigns more importance to the degraded feature after feature refinement during optimization. Extensive quantitative and qualitative experiments on four polyp benchmarks demonstrate the effectiveness of our method for automatic polyp segmentation. Our code is available at
https://github.com/TFboys-lzz/FoBS
.