RD-Net: Residual-Dense Network for Glaucoma Prediction Using Structural Features of Optic Nerve Head

Abstract

Glaucoma is called as the silent thief of eyesight. It is related to the internal damage of optical nerve head (ONH). For early screening, the simplest way is to analyze the subtle variations in structural features such as cup to disc ratio (CDR), disc damage likelihood scale (DDLS), rim width of the inferior, superior, nasal, and temporal (ISNT) regions of ONH. This can be done by accurate segmentation of optic disc (OD) and optic cup (OC). In this work, we have introduced a deep learning framework, called residual dense network (RD-NET), for disc and cup segmentation. Based on the segmentation results, the structural features are calculated. The proposed design differs from the traditional U-Net in that it utilizes filters with variable sizes and an alternative optimization method throughout the up- and down-sampling processes. The introduced method is a hybrid deep learning model that incorporates dense residual blocks and squeeze excitation block introduced within the conventional U-Net architecture. Unlike the classical approaches that are primarily based on CDR calculation, in this work, we first segment OD and OC using RD-Net and then analyze ISNT and DDLS. Once a suspicious case is detected, we then go for CDR calculation. In addition to developing an efficient segmentation model, six distinct kinds of data augmentation techniques have been also used in this study to increase the amount of training data. This, in turn, leads to a better estimation of model parameters. The model is rigorously trained and tested on four benchmark datasets namely DRISHTI, RIMONE, ORIGA, and REFUGE. Subsequently, the structural parameters are calculated for glaucoma prediction. The average accuracies are observed to be 0.9940 and 0.9894 for OD and cup segmentation, respectively. The extensive experiments presented in this article show that our method outperforms other existing state-of-the art algorithms.