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Angle closure glaucoma (ACG) is a more aggressive disease than open-angleglaucoma, where the abnormal anatomical structures of the anterior chamberangle (ACA) may cause an elevated intraocular pressure and gradually lead toglaucomatous optic neuropathy and eventually to visual impairment andblindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imagingprovides a fast and contactless way to discriminate angle closure from openangle. Although many medical image analysis algorithms have been developed forglaucoma diagnosis, only a few studies have focused on AS-OCT imaging. Inparticular, there is no public AS-OCT dataset available for evaluating theexisting methods in a uniform way, which limits progress in the development ofautomated techniques for angle closure detection and assessment. To addressthis, we organized the Angle closure Glaucoma Evaluation challenge (AGE), heldin conjunction with MICCAI 2019. The AGE challenge consisted of two tasks:scleral spur localization and angle closure classification. For this challenge,we released a large dataset of 4800 annotated AS-OCT images from 199 patients,and also proposed an evaluation framework to benchmark and compare differentmodels. During the AGE challenge, over 200 teams registered online, and morethan 1100 results were submitted for online evaluation. Finally, eight teamsparticipated in the onsite challenge. In this paper, we summarize these eightonsite challenge methods and analyze their corresponding results for the twotasks. We further discuss limitations and future directions. In the AGEchallenge, the top-performing approach had an average Euclidean Distance of 10pixels (10um) in scleral spur localization, while in the task of angle closureclassification, all the algorithms achieved satisfactory performances, with twobest obtaining an accuracy rate of 100%.

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography