A Robust Method for Automated Segmentation of Optic Disc Using Hypercolumn Deep Features With Probability Thresholding

Abstract

<jats:title>ABSTRACT</jats:title> <jats:p>Glaucoma is a dangerous disease that can lead to blindness in advanced stages. It has been a hot topic among machine learning researchers as it can be diagnosed quickly and effectively through optic disc segmentation. However, anomalies in the optic disk region significantly complicate this task. There is also a need for a model that produces stable results on different data sets. Therefore, in this study, we propose a novel method that can be used for early diagnosis and treatment of glaucoma. Using deep learning architecture, hypercolumn deep features extracted from retinal fundus images were trained with different classifiers, and their behavior was examined in depth according to varying thresholding values. Global and local thresholding approaches were developed to improve the predictions of the standard classifier. The proposed model achieved Dice scores of 0.9437, 0.9654, and 0.9407 on the DRIONS, DRISHTI, and RIMONE‐v3 datasets, respectively. The results obtained at the end of the study show that the proposed model is robust on these datasets and is competitive with other studies in the literature. In conclusion, we showed that the proposed model can be used effectively in glaucoma diagnosis.</jats:p>