Deep Neural Networks Application for Cup-to-Disc Ratio Estimation in Eye Fundus Images

Glaucoma is the second eye disease causing blindness worldwide. Optic Cup-to-Disc ratio (CDR) is a commonly applied method in glaucoma detection. The CDR is calculated based on Optic Disc (OD) and Optic Cup (OC) in eye fundus image screening. Therefore, the accurate segmentation of these two parameters is very important. Lately, Deep Neural Networks have demonstrated great effort in automated Optic Disc and Optic Cup segmentation but the overlapping between regions of OC and OD cause the challenge to obtain CDR automatically with high accuracy. In this paper, we assess the performance of CDR evaluation on three modifications of the Convolutional Neural Network (CNN) U-Net, namely Attention U-Net, Residual Attention U-Net (RAUNet), and U-Net++ applied on publicly available datasets RIM-ONE, DRISHTI, and REFUGE. We calculated the ground truth CDR value of testing eye fundus images of these datasets and compared it with the CDR value obtained by trained CNNs. Our results show that Attention U-net obtains the closest CDR to the ground truth CDR value but the identification of early-stage glaucoma needs an improvement.