REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

Abstract

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (\url{https://refuge.grand-challenge.org}), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.

Accepted for publication in Medical Image Analysis

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
• Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Cited by in corpus (6)

• Affinity Space Adaptation for Semantic Segmentation Across Domains
• Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
• Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection
• Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling
• MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint
• Fundus Image Analysis for Age Related Macular Degeneration: ADAM-2020 Challenge Report