Glaucoma screening pipeline based on clinical measurements and hidden features

Abstract

Glaucoma refers to a chronic disease of the eye that leads to vision loss that is irreversible, which is called ‘silent theft of sight’. Thus, an automatic glaucoma screening pipeline from optic disc (OD) localisation to glaucoma risk prediction is proposed in this study. The proposed pipeline consists of three main phases. Firstly, the OD is localised by morphological processing and sliding window methods. Secondly, a novel neural network which is in U‐shape and convolutional introduces concatenating path and fusion loss function is developed to split OD and optic cup (OC) at the same time. Thirdly, both clinical measurements including optic cup‐to‐disc ratio (CDR), neuroretinal rim related features, and hidden features including statistical moments, entropy and energy are combined to train glaucoma classifiers. According to the results of the experiment, the proposed segmentation network achieves the best performance on both OD and OC segmentation and the proposed CDR calculation method is capable of achieving the performance similar to that of ophthalmologist on CDR measurement. Besides, the authors’ glaucoma classification model can obtain the best performance on sensitivity and area under the curve score in comparison with the existing methods.