  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.
