Boundary-Guided Feature-Aligned Network for Colorectal Polyp Segmentation

Colorectal polyp segmentation in endoscopic images is very important for the prevention and treatment of colorectal cancer. Because of the high similarity between polyps and their surrounding tissues, most deep neural network (DNN) based methods often struggle with blurry boundaries and result in inaccurate segmentation. In this paper, we propose a Boundary-guided Feature-aligned Network (BFNet) for polyp segmentation by taking a boundary prediction task as an auxiliary. Firstly, BFNet aggregates multi-layer features extracted from the backbone to mine boundary cues. Secondly, a flexible feature aggregation (FFA) module is used at each layer to adaptively fuse cross-layer features for coarse polyp localization. In the FFA module, considering the spatial misalignment between features at different layers, the feature of the high layer is aligned to and fused with that of the current layer using the deformable convolution and flexible merge block. After that, a boundary-guided feature enhancement (BFE) module is applied to refine the localization at boundary areas. In the BFE module, the boundary information is extracted and highlighted in both channel and spatial dimensions using the attention mechanisms with the assistance of boundary cues. By applying deep supervision to the BFE modules, BFNet can produce accurate polyp segmentation. Experimental results show that our BFNet outperforms 14 state-of-the-art DNN-based polyp segmentation methods on both in-domain and out-of-domain tests.