Progressive Feature Enhancement Network for Automated Colorectal Polyp Segmentation

In recent years, colorectal polyp segmentation has attracted increasing attention in academia and industry. Although most existing methods can achieve commendable outcomes, they often confront difficulty when localizing challenging polyps with complex background, variable shape/size, and ambiguous boundary, because of the limitations in modeling global context and in cross-layer feature interaction. To cope with these challenges, this paper proposes a novel Progressive Feature Enhancement Network (PFENet) for polyp segmentation. Specifically, PFENet follows an encoder-decoder structure and utilizes the pyramid vision transformer as the encoder to capture multi-scale long-term dependencies at different stages. A cross-stage feature enhancement (CFE) module is embedded in each stage. The CFE module enhances the feature representation ability from interaction among adjacent stages, which helps integrate scale information for recognizing polyps with complex background and variable shape/size. In addition, a foreground boundary co-enhancement (FBC) module is used at each decoder to simultaneously enhance the foreground and boundary information by incorporating the output of the adjacent high stage and the coarse segmentation map, which is generated by fusing features of all four stages via a coarse map generation module. Through top-down connections of FBC modules, PFENet can progressively refine the prediction in a coarse-to-fine manner. Extensive experiments show the effectiveness of our PFENet in the polyp segmentation task, with the mIoU and mDic values over 0.886 and 0.931 tested on two in-domain datasets and over 0.735 and 0.809 tested on three out-of-domain datasets.Note to Practitioners—Automated and accurate polyp segmentation in colonoscopy images is a critical prerequisite for subsequent detection, removal, and diagnosis of polyps in clinical practice. This paper proposes a novel deep neural network for polyp segmentation, termed PFENet, with a CFE module to enhance the feature representation ability for better capturing polyps with complex background and variable shape/size, and a FBC module to simultaneously enhance the foreground and boundary information on the feature representation provided by the CFE module. Qualitative and quantitative results on five public datasets show that our PFENet yields accurate predictions and is superior to 9 state-of-the-art polyp segmentation methods. The proposed PFENet will facilitate potential computer-aided diagnosis systems in clinical practice, in which it can better promote medical decision-making than competing methods in polyp detection and removal.