Edge-Guided Bidirectional-Attention Residual Network for Polyp Segmentation

Precise polyp segmentation provides important information in the early detection of colorectal cancer in clinical practice. However, it is a challenging task for two major reasons: 1) the color and texture of polyps are very similar to surrounding mucosa especially in the edge area; 2) the polyps often vary largely in scale, shape and location. To this end, we propose an edge-guided bidirectional-attention residual network (EBRNet) equipped with an edge-guided bidirectional-attention residual module (EBRM) and a context enrichment layer (CEL). The proposed EBRM focuses on both foreground and background regions for detail recovery and noise suppression to capture the camouflaged polyps in cluttered tissue, and introduces edge cues for accurate boundaries. The CEL enriches the contextual semantics in multiple levels to adaptively detect the polyps in various sizes, shapes and locations. Extensive experiments on five benchmark datasets demonstrate that our EBRNet performs favorably against most state-of-the-art methods under different evaluation metrics. The source code will be publicly available at https://github.com/LanhooNg/EBRNet.