Construction of Retinal Vessel Segmentation Models Based on Convolutional Neural Network

摘要

Segmentation of retinal vessels in fundus images plays a very important role in diagnosing relevant diseases. In this paper, we have constructed automated segmentation models for the retinal vessel segmentation task based on convolutional neural networks. Since some typical deep convolutional neural networks need to be fed by high-resolution patches, small retinal patches should be interpolated to the specific resolution. The interpolated patches sometimes would introduce additional noises. Thus, we modify some typical deep architectures by inserting a set of convolutional layers. In this way, our models have the ability to adapt to different resolutions. Overall, five models are analyzed and compared in our studies including LeNet, M-AlexNet (modified AlexNet), M-ZF-Net (Modified ZF-Net), M-VGG (Modified VGG) and Deformable-ConvNet. Deformable-ConvNet captures the vascular structure and is used to do the retinal vessel segmentation task for the first time. We train the models from scratch and compare their ability to discriminate vessels/non-vessel pixels on two retinal fundus image datasets, DRIVE and STARE. Results are analyzed and compared in our studies. We obtain the highest accuracy of 0.9628/0.9690, lowest loss of 0.1045/0.0968, and highest AUC of 0.9764/0.9844 on DRIVE/STARE respectively. We also compare the CNN models with other segmentation methods. The results demonstrate the high effectiveness of the CNN-based approaches.