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The segmentation of retinal vessels is of significance for doctors todiagnose the fundus diseases. However, existing methods have various problemsin the segmentation of the retinal vessels, such as insufficient segmentationof retinal vessels, weak anti-noise interference ability, and sensitivity tolesions, etc. Aiming to the shortcomings of existed methods, this paperproposes the use of conditional deep convolutional generative adversarialnetworks to segment the retinal vessels. We mainly improve the networkstructure of the generator. The introduction of the residual module at theconvolutional layer for residual learning makes the network structure sensitiveto changes in the output, as to better adjust the weight of the generator. Inorder to reduce the number of parameters and calculations, using a smallconvolution to halve the number of channels in the input signature before usinga large convolution kernel. By used skip connection to connect the output ofthe convolutional layer with the output of the deconvolution layer to avoidlow-level information sharing. By verifying the method on the DRIVE and STAREdatasets, the segmentation accuracy rate is 96.08% and 97.71%, the sensitivityreaches 82.74% and 85.34% respectively, and the F-measure reaches 82.08% and85.02% respectively. The sensitivity is 4.82% and 2.4% higher than that ofR2U-Net.

Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks