基于条件深度卷积生成对抗网络视网膜血管分割.PDF

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 （西北师范大学 计算机科学与工程学院，兰州 730000） 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，从而更好的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对输入特征图的通道数进行减半处理。通过使用跳远连接将卷积 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% ，敏感度分别达到了82.74%、85.34% ，F-measure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% 。 关键词 生成对抗网络，残差网络，视网膜血管分割，条件模型，卷积神经网络 Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks JIANG Yun, TAN Ning (School of Computer Science and Engineering, Northwest Normal University,Lanzhou 730000,China) 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 to halve the number of channels in the input signature before using a large convolution kernel. By used skip connection to