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跨级融合门控自适应网络用于视网膜血管分割

梁礼明 余洁 陈鑫 雷坤 周珑颂

梁礼明,余洁,陈鑫,等. 跨级融合门控自适应网络用于视网膜血管分割[J]. 北京航空航天大学学报,2024,50(4):1097-1109 doi: 10.13700/j.bh.1001-5965.2022.0410
引用本文: 梁礼明,余洁,陈鑫,等. 跨级融合门控自适应网络用于视网膜血管分割[J]. 北京航空航天大学学报,2024,50(4):1097-1109 doi: 10.13700/j.bh.1001-5965.2022.0410
LIANG L M,YU J,CHEN X,et al. Cross-level fusion gated adaptive network for retinal vessel segmentation[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1097-1109 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0410
Citation: LIANG L M,YU J,CHEN X,et al. Cross-level fusion gated adaptive network for retinal vessel segmentation[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1097-1109 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0410

跨级融合门控自适应网络用于视网膜血管分割

doi: 10.13700/j.bh.1001-5965.2022.0410
基金项目: 国家自然科学基金(51365017,61463018);江西省自然科学基金面上项目(20192BAB205084);江西省研究生创新专项资金(YC2021-S585);江西省教育厅科学技术项目(GJJ170491,GJJ2200848)
详细信息
    通讯作者:

    E-mail:9119890012@ jxust.edu.cn

  • 中图分类号: TP391

Cross-level fusion gated adaptive network for retinal vessel segmentation

Funds: National Natural Science Foundation of China (51365017,61463018);General Project of Natural Science Foundation of Jiangxi, China (20192BAB205084); Jiangxi Province Graduate Innovation Special Fund (YC2021-S585); Science and Technology Project of Jiangxi Provincial Department of Education (GJJ170491,GJJ2200848)
More Information
  • 摘要:

    针对现有多数算法对浅层特征提取不足,导致分割结果中血管边界模糊、毛细血管欠分割且包含噪声等问题,提出一种跨级融合门控自适应网络。该网络中的密集门控通道变换模块,通过促进通道之间的竞争或协同关系充分提取浅层特征信息,避免浅层粗粒度特征信息丢失;通过跨层次融合模块捕获各层跨维度交互信息,有效聚合多尺度上下文特征;采用双自适应特征融合方法有效引导相邻层次特征融合,抑制噪声。在公共数据集DRIVE、CHASEDB1和STARE上进行验证,结果表明:所提网络准确率分别为0.9652、0.9668和0.9695,F1值分别为0.8544、0.8152和0.8412,在多个指标上均处于较高水平,优于现有先进算法。

     

  • 图 1  GCT模块

    Figure 1.  GCT module

    图 2  DGCT模块

    Figure 2.  DGCT module

    图 3  三重注意力机制

    Figure 3.  Triple attention mechanism

    图 4  跨层次融合模块

    Figure 4.  Cross-level fusion module

    图 5  双自适应特征融合模块

    Figure 5.  Dual adaptive feature fusion module

    图 6  跨级融合门控自适应网络

    Figure 6.  Cross-level fusion gated adaptive network

    图 7  预处理各阶段效果

    Figure 7.  Effect of each stage of preprocessing

    图 8  不同算法在DRIVE数据集上ROC曲线与PR曲线对比

    Figure 8.  Comparison of ROC curves and PR curves of different algorithms on DRIVE dataset

    图 9  不同算法在CHASSEDB1数据集上ROC曲线与PR曲线对比

    Figure 9.  Comparison of ROC curves and PR curves of different algorithms on CHASSEDB1 dataset

    图 10  不同算法分割结果

    Figure 10.  Segmentation results of different algorithms

    图 11  分割结果细节对比

    Figure 11.  Details comparison of segmentation results

    图 12  模型改进分割图像结果对比

    Figure 12.  Comparison of image segmentation results of improved model

    表  1  不同注意力机制的参数复杂度和开销比较

    Table  1.   comparison of parameter complexity and overhead of various attentional mechanisms

    注意力机制 参数量 存储开销/106
    SE 2C2/r 2.514
    CBAM 2C2/r+2k2 2.532
    全局上下文 2C2/r+C 2.548
    TAM(本文) 6k2 0.004 8
    下载: 导出CSV

    表  2  不同数据集上的平均性能指标评估结果

    Table  2.   Average performance index evaluation results on different datasets

    数据集 算法 F1 Acc Se Sp AUC(ROC) AUC(PR)
    DRIVE U-Net[13] 0.8474 0.9631 0.8302 0.9812 0.9855 0.9310
    Attention U-Net[14] 0.8449 0.9627 0.8287 0.9814 0.9852 0.9299
    ASR-UNet[15] 0.8507 0.9641 0.8351 0.9821 0.9862 0.9336
    CFGA-Net(本文) 0.8544 0.9652 0.8326 0.9837 0.9873 0.9379
    CHASEDB1 U-Net[13] 0.8002 0.9641 0.7934 0.9811 0.9834 0.8848
    Attention U-Net[14] 0.8109 0.9658 0.8090 0.9813 0.9845 0.8942
    ASR-UNet[15] 0.8061 0.9650 0.8047 0.9809 0.9848 0.8916
    CFGA-Net(本文) 0.8152 0.9668 0.8097 0.9824 0.9866 0.9026
    STARE U-Net[13] 0.8192 0.9644 0.7591 0.9888 0.9843 0.9158
    Attention U-Net[14] 0.8184 0.9646 0.7522 0.9898 0.9836 0.9161
    ASR-UNet[15] 0.8239 0.9653 0.7649 0.9891 0.9853 0.9201
    CFGA-Net(本文) 0.8412 0.9695 0.8023 0.9886 0.9899 0.9347
    下载: 导出CSV

    表  3  不同算法在数据集DRIVE、CHASEDB1和STARE上的性能指标对比

    Table  3.   Comparison of performance indicators of different algorithms in DRIVE, CHASEDB1 and STARE datasets

    算法 Se Sp Acc
    DRIVE CHASEDB1 STARE DRIVE CHASEDB1 STARE DRIVE CHASEDB1 STARE
    文献[16] 0.7653 0.7633 0.7581 0.9818 0.9809 0.9846 0.9542 0.9610 0.9612
    文献[17] 0.7632 0.7815 0.7423 0.9536 0.9587 0.9603
    文献[18] 0.7631 0.7641 0.7735 0.9820 0.9806 0.9857 0.9538 0.9607 0.9638
    文献[19] 0.7918 0.6457 0.8021 0.9708 0.9653 0.9561 0.9577 0.9340 0.9445
    文献[20] 0.7941 0.8176 0.7598 0.9798 0.9704 0.9878 0.9558 0.9608 0.9640
    文献[21] 0.8213 0.8035 0.9807 0.9787 0.9615 0.9639
    文献[22] 0.7352 0.7279 0.7265 0.9775 0.9658 0.9759 0.9480 0.9452 0.9548
    文献[23] 0.8353 0.8176 0.7946 0.9751 0.9776 0.9821 0.9579 0.9632 0.9626
    文献[24] 0.8125 0.8012 0.8078 0.9763 0.9730 0.9721 0.9610 0.9578 0.9586
    CFGA-Net(本文) 0.8326 0.8097 0.8023 0.9837 0.9824 0.9886 0.9652 0.9668 0.9695
    下载: 导出CSV

    表  4  算法改进前后结果对比

    Table  4.   Comparison of results before and after improvement

    模型 F1 Acc Se Sp AUC
    DRIVE CHASEDB1 STARE DRIVE CHASEDB1 STARE DRIVE CHASEDB1 STARE DRIVE CHASEDB1 STARE DRIVE CHASEDB1 STARE
    1 0.8474 0.8002 0.8192 0.9631 0.9641 0.9644 0.8302 0.7934 0.7591 0.9812 0.9811 0.9888 0.9855 0.9834 0.9843
    2 0.8527 0.8139 0.8243 0.9648 0.9661 0.9659 0.8323 0.8193 0.7810 0.9833 0.9807 0.9873 0.9867 0.9861 0.9870
    3 0.8530 0.8122 0.8241 0.9647 0.9659 0.9652 0.8296 0.8134 0.7770 0.9826 0.9811 0.9879 0.9822 0.9854 0.9860
    4 0.8528 0.8136 0.8276 0.9649 0.9660 0.9665 0.8297 0.8223 0.7865 0.9839 0.9803 0.9874 0.9870 0.9860 0.9875
    5 0.8530 0.8132 0.8331 0.9650 0.9661 0.9672 0.8290 0.8138 0.7839 0.9840 0.9813 0.9886 0.9855 0.9859 0.9888
    6 0.8544 0.8152 0.8412 0.9652 0.9668 0.9695 0.8326 0.8097 0.8023 0.9837 0.9824 0.9886 0.9873 0.9866 0.9899
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-21
  • 录用日期:  2022-08-05
  • 网络出版日期:  2022-08-23
  • 整期出版日期:  2024-04-29

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