Retinal Vessel Segmentation Method Based on Improved Deep U-Net

The automatic segmentation of retinal vessels plays an important role in the early screening of eye diseases. However, vessels are difficult to segment with pathological retinal images. Hence, we propose the use of deep U-net, a new retinal vessel segmentation method based on an improved U-shaped fully convolutional neural network. The method uses not only local features learned from the shallow convolution layers, but also abstract features learned from deep convolution layers. To improve the segmentation accuracy for thin vessels, we applied Gaussian matched filtering to the U-net. The batch normalization layer was added in the U-net network, which increased the speed of convergence. In the training phase, a new sample amplification method called translation-reflection was proposed to increase the proportion of blood vessels in the training images. Results of the experiments showed that the proposed method leads to better retinal vessel segmentation than other methods developed in recent years do for the SE, SP, Acc, Ppv, and AUC evaluation metrics.