Boundary Refinement Network for Colorectal Polyp Segmentation in Colonoscopy Images

Precise polyp segmentation is vitally essential for detection and diagnosis of early colorectal cancer. Recent advances in artificial intelligence have brought infinite possibilities for this task. However, polyps usually vary greatly in shape and size and contain ambiguous boundary, bringing tough challenges to precise segmentation. In this letter, we introduce a novel Boundary Refinement Network (BRNet) for polyp segmentation. To be specific, we first introduce a boundary generation module (BGM) to generate boundary map by fusing both low-level spatial details and high-level concepts. Then, we utilize the boundary-guided refinement module to refine the polyp-aware features at each layer with the help of boundary cues from the BGM and the prediction from the adjacent high layer. Through top-down deep supervision, our BRNet can localize the polyp regions accurately with clear boundary. Extensive experiments are carried out on five datasets, and the results indicate the effectiveness of our BRNet over seven recently reported methods.