BA-Net: Brightness prior guided attention network for colonic polyp segmentation

• Published in: Biocybernetics and biomedical engineering Vol. 43; no. 3; pp. 603 - 615
• Main Authors: Xia, Haiying, Qin, Yilin, Tan, Yumei, Song, Shuxiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2023
• Subjects: Brightness prior knowledge; Colonic polyp segmentation; Computer-aided diagnosis; Deep learning; Global reverse attention; Deep learning; Brightness prior knowledge; Colonic polyp segmentation; Global reverse attention; Computer-aided diagnosis
• ISSN: 0208-5216
• DOI: 10.1016/j.bbe.2023.08.001

Automatic polyp segmentation at colonoscopy plays an important role in the early diagnosis and surgery of colorectal cancer. However, the diversity of polyps in different images greatly increases the difficulty of accurately segmenting polyps. Manual segmentation of polyps in colonoscopic images is time-consuming and the rate of polyps missed remains high. In this paper, we propose a brightness prior guided attention network (BA-Net) for automatic polyp segmentation. Specifically, we first aggregate the high-level features of the last three layers of the encoder with an enhanced receptive field (ERF) module, which further fed to the decoder to obtain the initial prediction maps. Then, we introduce a brightness prior fusion (BF) module that fuses the brightness prior information into the multi-scale side-out high-level semantic features. The BF module aims to induce the network to localize salient regions, which may be potential polyps, to obtain better segmentation results. Finally, we propose a global reverse attention (GRA) module to combine the output of the BF module and the initial prediction map for obtaining long-range dependence and reverse refinement prediction results. With iterative refinement from higher-level semantics to lower-level semantics, our BA-Net can achieve more refined and accurate segmentation. Extensive experiments show that our BA-Net outperforms the state-of-the-art methods on six common polyp datasets.