MCPANet: Multiscale Cross-Position Attention Network for Retinal Vessel Image Segmentation

被引:5
|
作者
Jiang, Yun [1 ]
Liang, Jing [1 ]
Cheng, Tongtong [1 ]
Zhang, Yuan [1 ]
Lin, Xin [1 ]
Dong, Jinkun [1 ]
机构
[1] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China
来源
SYMMETRY-BASEL | 2022年 / 14卷 / 07期
基金
中国国家自然科学基金;
关键词
retinal vessel segmentation; convolutional neural network; attention mechanism; NET;
D O I
10.3390/sym14071357
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Accurate medical imaging segmentation of the retinal fundus vasculature is essential to assist physicians in diagnosis and treatment. In recent years, convolutional neural networks (CNN) are widely used to classify retinal blood vessel pixels for retinal blood vessel segmentation tasks. However, the convolutional block receptive field is limited, simple multiple superpositions tend to cause information loss, and there are limitations in feature extraction as well as vessel segmentation. To address these problems, this paper proposes a new retinal vessel segmentation network based on U-Net, which is called multi-scale cross-position attention network (MCPANet). MCPANet uses multiple scales of input to compensate for image detail information and applies to skip connections between encoding blocks and decoding blocks to ensure information transfer while effectively reducing noise. We propose a cross-position attention module to link the positional relationships between pixels and obtain global contextual information, which enables the model to segment not only the fine capillaries but also clear vessel edges. At the same time, multiple scale pooling operations are used to expand the receptive field and enhance feature extraction. It further reduces pixel classification errors and eases the segmentation difficulty caused by the asymmetry of fundus blood vessel distribution. We trained and validated our proposed model on three publicly available datasets, DRIVE, CHASE, and STARE, which obtained segmentation accuracy of 97.05%, 97.58%, and 97.68%, and Dice of 83.15%, 81.48%, and 85.05%, respectively. The results demonstrate that the proposed method in this paper achieves better results in terms of performance and segmentation results when compared with existing methods.
引用
收藏
页数:19
相关论文
共 50 条
  • [31] Lightweight pyramid network with spatial attention mechanism for accurate retinal vessel segmentation
    Tengfei Tan
    Zhilun Wang
    Hongwei Du
    Jinzhang Xu
    Bensheng Qiu
    [J]. International Journal of Computer Assisted Radiology and Surgery, 2021, 16 : 673 - 682
  • [32] CFFANet: category feature fusion and attention mechanism network for retinal vessel segmentation
    Chen, Qiyu
    Wang, Jianming
    Yin, Jiting
    Yang, Zizhong
    [J]. Multimedia Systems, 2024, 30 (06)
  • [33] NAUNet: lightweight retinal vessel segmentation network with nested connections and efficient attention
    Yang, Dongxu
    Zhao, Hongdong
    Yu, Kuaikuai
    Geng, Lixin
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (16) : 25357 - 25379
  • [34] NAUNet: lightweight retinal vessel segmentation network with nested connections and efficient attention
    Dongxu Yang
    Hongdong Zhao
    Kuaikuai Yu
    Lixin Geng
    [J]. Multimedia Tools and Applications, 2023, 82 : 25357 - 25379
  • [35] EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
    Wang, Jun
    Zhao, Yang
    Qian, Linglong
    Yu, Xiaohan
    Gao, Yongsheng
    [J]. 2021 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2021), 2021, : 161 - 167
  • [36] RNA-Net: Residual Nonlocal Attention Network for Retinal Vessel Segmentation
    Chen, Yixuan
    Dong, Yuhan
    Zhang, Yi
    Zhang, Kai
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 1560 - 1565
  • [37] Bi-SANet-Bilateral Network with Scale Attention for Retinal Vessel Segmentation
    Jiang, Yun
    Yao, Huixia
    Ma, Zeqi
    Zhang, Jingyao
    [J]. SYMMETRY-BASEL, 2021, 13 (10):
  • [38] SAF-Net:Self Attention Fusion Network for Retinal Vessel Segmentation
    Liu, Na
    Wang, Guoqiang
    [J]. Computer Engineering and Applications, 2023, 59 (14) : 217 - 223
  • [39] Lightweight pyramid network with spatial attention mechanism for accurate retinal vessel segmentation
    Tan, Tengfei
    Wang, Zhilun
    Du, Hongwei
    Xu, Jinzhang
    Qiu, Bensheng
    [J]. INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2021, 16 (04) : 673 - 682
  • [40] RFARN: Retinal vessel segmentation based on reverse fusion attention residual network
    Liu, Wenhuan
    Jiang, Yun
    Zhang, Jingyao
    Ma, Zeqi
    [J]. PLOS ONE, 2021, 16 (12):