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

被引:4
|
作者
Xia, Haiying [1 ]
Qin, Yilin [1 ]
Tan, Yumei [2 ]
Song, Shuxiang [1 ]
机构
[1] Guangxi Normal Univ, Coll Elect Engn, Guilin 541004, Peoples R China
[2] Guangxi Normal Univ, Sch Comp Sci & Engn, Guilin 541004, Peoples R China
关键词
Deep learning; Computer-aided diagnosis; Colonic polyp segmentation; Global reverse attention; Brightness prior knowledge;
D O I
10.1016/j.bbe.2023.08.001
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Automatic polyp segmentation at colonoscopy plays an important role in the early diagno-sis 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 net-work to localize salient regions, which may be potential polyps, to obtain better segmenta-tion 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 depen-dence and reverse refinement prediction results. With iterative refinement from higher-level semantics to lower-level semantics, our BA-Net can achieve more refined and accu-rate segmentation. Extensive experiments show that our BA-Net outperforms the state -of-the-art methods on six common polyp datasets.& COPY; 2023 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:603 / 615
页数:13
相关论文
共 50 条
  • [41] CGBA-Net: context-guided bidirectional attention network for surgical instrument segmentation
    Wang, Yiming
    Hu, Yan
    Shen, Junyong
    Zhang, Xiaoqing
    Li, Heng
    Qiu, Zhongxi
    Ye, Fangfu
    Liu, Jiang
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2023, 18 (10) : 1769 - 1781
  • [42] CGBA-Net: context-guided bidirectional attention network for surgical instrument segmentation
    Yiming Wang
    Yan Hu
    Junyong Shen
    Xiaoqing Zhang
    Heng Li
    Zhongxi Qiu
    Fangfu Ye
    Jiang Liu
    International Journal of Computer Assisted Radiology and Surgery, 2023, 18 : 1769 - 1781
  • [43] Boundary-Guided Integrated Context Network for Polyp Segmentation
    Zhao, Haifeng
    Li, Yuheng
    Yu, Yue
    Zhang, Shaojie
    PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CRYPTOGRAPHY, NETWORK SECURITY AND COMMUNICATION TECHNOLOGY, CNSCT 2024, 2024, : 415 - 420
  • [44] Attention Guided Network for Retinal Image Segmentation
    Zhang, Shihao
    Fu, Huazhu
    Yan, Yuguang
    Zhang, Yubing
    Wu, Qingyao
    Yang, Ming
    Tan, Mingkui
    Xu, Yanwu
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT I, 2019, 11764 : 797 - 805
  • [45] Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion
    Huang, Xiaodong
    Zhuo, Li
    Zhang, Hui
    Yang, Yang
    Li, Xiaoguang
    Zhang, Jing
    Wei, Wei
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2022, 98
  • [46] Attention guided U-Net for accurate iris segmentation
    Lian, Sheng
    Luo, Zhiming
    Zhong, Zhun
    Lin, Xiang
    Su, Songzhi
    Li, Shaozi
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2018, 56 : 296 - 304
  • [47] Enhanced U-Net: A Feature Enhancement Network for Polyp Segmentation
    Patel, Krushi
    Bur, Andres M.
    Wang, Guanghui
    2021 18TH CONFERENCE ON ROBOTS AND VISION (CRV 2021), 2021, : 181 - 188
  • [48] BLE-Net: boundary learning and enhancement network for polyp segmentation
    Ta, Na
    Chen, Haipeng
    Lyu, Yingda
    Wu, Taosuo
    MULTIMEDIA SYSTEMS, 2023, 29 (05) : 3041 - 3054
  • [49] BLE-Net: boundary learning and enhancement network for polyp segmentation
    Na Ta
    Haipeng Chen
    Yingda Lyu
    Taosuo Wu
    Multimedia Systems, 2023, 29 : 3041 - 3054
  • [50] UCFA-Net: A U-shaped cross-fusion network with attention mechanism for enhanced polyp segmentation
    Wang, Shuai
    Zhao, Tiejun
    Wang, Guocun
    Han, Ye
    Wu, Fan
    IET IMAGE PROCESSING, 2025, 19 (01)