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基于条件深度卷积生成对抗网络的视网膜血管分割

蒋芸 谭宁

蒋芸, 谭宁.基于条件深度卷积生成对抗网络的视网膜血管分割.自动化学报, 2021, 47(1): 136−147 doi: 10.16383/j.aas.c180285
引用本文: 蒋芸, 谭宁.基于条件深度卷积生成对抗网络的视网膜血管分割.自动化学报, 2021, 47(1): 136−147 doi: 10.16383/j.aas.c180285
Jiang Yun, Tan Ning. Retinal vessel segmentation based on conditional deep convolutional generative adversarial networks. Acta Automatica Sinica, 2021, 47(1): 136−147 doi: 10.16383/j.aas.c180285
Citation: Jiang Yun, Tan Ning. Retinal vessel segmentation based on conditional deep convolutional generative adversarial networks. Acta Automatica Sinica, 2021, 47(1): 136−147 doi: 10.16383/j.aas.c180285

基于条件深度卷积生成对抗网络的视网膜血管分割

doi: 10.16383/j.aas.c180285
基金项目: 

国家自然科学基金 61962054

国家自然科学基金 61163036

2016年甘肃省科技计划资助自然科学基金项目 1606RJZA047

甘肃省高校研究生导师项目 1201-16

西北师范大学第三期知识与创新工程科研骨干项目 nwnu-kjcxgc-03-67

详细信息
    作者简介:

    蒋芸  工学博士.西北师范大学计算机科学与工程学院教授.主要研究方向为数据挖掘, 粗糙集理论及应用.E-mail: jiangyun@nwnu.edu.cn

    通讯作者:

    谭宁  西北师范大学计算机科学与工程学院硕士研究生.主要研究方向为深度学习, 医学图像处理.本文通信作者.E-mail: tanning2315@126.com

  • 本文责任编委 黄庆明

Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks

Funds: 

National Natural Science Foundation of China 61962054

National Natural Science Foundation of China 61163036

2016 Gansu Provincial Science and Technology Plan Funded Natural Science Fund Project 1606RJZA047

Gansu Provincial University Graduate Tutor Project 1201-16

The third phase of the Northwest Normal University Knowledge and Innovation Engineering Research Backbone Project nwnu-kjcxgc-03-67

More Information
    Author Bio:

    JIANG Yun   Ph. D., professor at the College of Computer Science and Engineering, Northwest Normal University. Her research interest covers data mining, rough set theory and application

    Corresponding author: TAN Ning   Master student at the College of Computer Science and Engineering, Northwest Normal University. His research interest covers deep learning, medical image processing. Corresponding author of this paper
  • Recommended by Associate Editor HUANG Qing-Ming
  • 摘要: 视网膜血管的分割帮助医生对眼底疾病进行诊断有着重要的意义.但现有方法对视网膜血管的分割存在着各种问题, 例如对血管分割不足, 抗噪声干扰能力弱, 对病灶敏感等.针对现有血管分割方法的缺陷, 本文提出使用条件深度卷积生成对抗网络的方法对视网膜血管进行分割.我们主要对生成器的网络结构进行了改进,在卷积层引入残差模块进行差值学习使得网络结构对输出的改变变得敏感, 从而更好地对生成器的权重进行调整.为了降低参数数目和计算, 在使用大卷积核之前使用小卷积核对输入特征图的通道数进行减半处理.通过使用U型网络的思想将卷积层的输出与反卷积层的输出进行连接从而避免低级信息共享.通过在DRIVE和STARE数据集上对本文的方法进行了验证, 其分割准确率分别为96.08 %、97.71 %, 灵敏性分别达到了82.74 %、85.34 %, $F$度量分别达到了82.08 %和85.02 %, 灵敏度比R2U-Net的灵敏度分别高了4.82 %, 2.4 %.
    Recommended by Associate Editor HUANG Qing-Ming
    1)  本文责任编委 黄庆明
  • 图  1  视网膜血管图像分割模型

    Fig.  1  Retinal vessels image segmentation model

    图  2  条件生成对抗网络模型

    Fig.  2  Condition generation adversarial networks model

    图  3  卷积层

    Fig.  3  Convolution layer

    图  4  卷积层

    Fig.  4  Convolution layer

    图  5  生成器网络结构

    Fig.  5  Generator network structure

    图  6  卷积的不同变体

    Fig.  6  Different variants of convolutional

    图  7  判别器网络结构

    Fig.  7  Discriminator network structure

    图  8  生成器训练的过程

    Fig.  8  Generative training process

    图  9  判别器训练的过程

    Fig.  9  Discriminator training process

    图  10  DRIVE数据库视网膜血管分割结果比较

    Fig.  10  Comparisons of segmentation results on DRIVE database

    图  11  STARE数据库视网膜血管分割结果比较

    Fig.  11  Comparisons of segmentation results on STARE database

    图  12  不同算法的视网膜血管分割局部放大图

    Fig.  12  Different methods of partial retinal vessel segmentation

    图  13  不同算法的$F$度量性能评价曲线

    Fig.  13  Different methods of $F$-measure performance evaluation curve

    表  1  模型改进前后分割的结果

    Table  1  The segmentation results before and after model improvement

    数据集 方法 $F$度量 准确率
    DRIVE/STARE U-net 0.8142/0.8373 0.9531/0.9690
    GAN+U-net 0.8150/0.8398 0.9583/0.9710
    U-net+Residual 0.8149/0.8388 0.9553/0.9700
    GAN+U-net+Residual (本文结构) 0.8208/0.8506 0.9608/0.9771
    下载: 导出CSV

    表  2  使用瓶颈层前后分割的结果

    Table  2  The result of segmentation before and after using the bottleneck layer

    数据集 方法 参数 计算量$(GFLOPS)$ $F$度量 准确率
    DRIVE/STARE No Bottleneck 19.8 M 183.8 0.8210/0.8504 0.9612/0.9772
    Bottleneck 5.2 M 48.5 0.8208/0.8502 0.9608/0.9771
    下载: 导出CSV

    表  3  DRIVE数据库视网膜血管分割结果

    Table  3  Segmentation performance of retinal vessel on the DRIVE database

    数据集 方法 年份 $F$度量 灵敏性 特效性 准确率
    DRIVE Chen[13] 2014 0.7252 0.9798 0.9474
    N$^4$-Fields[30] 2014 0.7970 0.8437 0.9743 0.9626
    Azzopardi[5] 2015 0.7655 0.9704 0.9442
    Roychowdhury[12] 2016 0.7250 0.9830 0.9520
    Liskowsk[14] 2016 0.7763 0.9768 0.9495
    Qiaoliang Li[12] 2016 0.7569 0.9816 0.9527
    DRIU[27] 2016 0.6701 0.9696 0.9115 0.9165
    HED[28] 2017 0.6400 0.9563 0.9007 0.9054
    U-Net[33] 2018 0.8142 0.7537 0.9820 0.9531
    Residual U-Net[33] 2018 0.8149 0.7726 0.9820 0.9553
    Recurrent U-Net[33] 2018 0.8155 0.7751 0.9816 0.9556
    R2U-Net[33] 2018 0.8171 0.7792 0.9813 0.9556
    本文方法 2018 0.8208 0.8274 0.9775 0.9608
    下载: 导出CSV

    表  4  STARE数据库视网膜血管分割结果

    Table  4  Segmentation performance of retinal vessel on the STARE database

    数据集 方法 年份 $F$度量 灵敏性 特效性 准确率
    STARE Marin[31] 2011 0.6940 0.9770 0.9520
    Fraz[32] 2012 0.7548 0.9763 0.9534
    Liskowsk[14] 2016 0.7867 0.9754 0.9566
    Roychowdhury[12] 2016 0.7720 0.9730 0.9510
    Qiaoliang Li[12] 2016 0.7726 0.9844 0.9628
    DRIU[27] 2016 0.7385 0.6066 0.9956 0.9499
    HED[28] 2017 0.6990 0.5555 0.9955 0.9378
    U-Net[33] 2018 0.8373 0.8270 0.9842 0.9690
    Residual U-Net[33] 2018 0.8388 0.8203 0.9856 0.9700
    Recurrent U-Net[33] 2018 0.8396 0.8108 0.9871 0.9706
    R2U-Net[33] 2018 0.8475 0.8298 0.9862 0.9712
    本文方法 2018 0.8502 0.8538 0.9878 0.9771
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-07
  • 录用日期:  2018-08-02
  • 刊出日期:  2021-01-29

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