Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks
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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 %.Abstract: The segmentation of retinal vessels is of significance for doctors to diagnose the fundus diseases. However, existing methods have various problems in the segmentation of the retinal vessels, such as insufficient segmentation of retinal vessels, weak anti-noise interference ability, and sensitivity to lesions, etc. Aiming to the shortcomings of existed methods, this paper proposes the use of conditional deep convolutional generative adversarial networks to segment the retinal vessels. We mainly improve the network structure of the generator. The introduction of the residual module at the convolutional layer for residual learning makes the network structure sensitive to changes in the output, as to better adjust the weight of the generator. In order to reduce the number of parameters and calculations, using a small convolution kernel to halve the number of channels in the input signature before using a large convolution kernel. By used the idea of a U-net to connect the output of the convolutional layer with the output of the deconvolution layer to avoid low-level information sharing. By verifying the method on the DRIVE and STARE datasets, the segmentation accuracy rate is 96.08 % and 97.71 %, the sensitivity reaches 82.74 % and 85.34 %, respectively, and the $F$-measure reaches 82.08 % and 85.02 %, respectively. The sensitivity is 4.82 % and 2.4 % higher than that of R2U-Net.
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Key words:
- Generative adversarial network (GAN) /
- residual networks /
- retinal vessel segmentation /
- conditional models /
- convolutional neural networks (CNNs)
1) 本文责任编委 黄庆明 -
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 
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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
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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
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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
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