Abstract：Glaucoma is the world's largest irreversible blindness eye disease. Early diagnosis and timely treatment are effective measures to prevent blindness caused by glaucoma. The cup to disc ratio in fundus images is an important index of early screening and clinical diagnosis of glaucoma. Therefore, accurate segmentation of the optic cup and disc is the key to accurately calculate the cup to disc ratio and improve the computer-aided diagnosis technology of glaucoma. To solve this problem, this paper firstly performed a polar coordinate transformation preprocessing on the fundus image, and then proposed a convolutional neural network Seg-RFNet that integrated the receptive field module to achieve joint segmentation of the optic cup and disc. Seg-RFNet was based on SegNet framework and used ResNet50 as the coding layer to enhance the feature extraction ability of the image, and the coding layer was branched to obtain more deep semantic information. At the same time, the receptive field module was added between the coding layer and decoding layer, which was able to simulate the human visual system, increasing the receptive field and enhance the response of useful features. The 800 fundus images from REFUGE that is a dataset published in MICCAI 2018, were used to verify the performance of the proposed method compared with other methods. The results showed that the Jaccard Similarity (higher is better) of the optic cup and disk segmentation was 0.9515 and 0.8720, and the F score (higher is better) was 0.9749 and 0.9301, respectively. Compared with the commonly used U-Net, SegNet and other networks, Seg-RFNet showed better joint segmentation accuracy of the optic cup and disc and provided an accurate segmentation basis for calculating the cup to disc ratio.
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