  Angle closure glaucoma (ACG) is a more aggressive disease than open-angle
glaucoma, where the abnormal anatomical structures of the anterior chamber
angle (ACA) may cause an elevated intraocular pressure and gradually lead to
glaucomatous optic neuropathy and eventually to visual impairment and
blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging
provides a fast and contactless way to discriminate angle closure from open
angle. Although many medical image analysis algorithms have been developed for
glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In
particular, there is no public AS-OCT dataset available for evaluating the
existing methods in a uniform way, which limits progress in the development of
automated techniques for angle closure detection and assessment. To address
this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held
in 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 different
models. During the AGE challenge, over 200 teams registered online, and more
than 1100 results were submitted for online evaluation. Finally, eight teams
participated in the onsite challenge. In this paper, we summarize these eight
onsite challenge methods and analyze their corresponding results for the two
tasks. We further discuss limitations and future directions. In the AGE
challenge, the top-performing approach had an average Euclidean Distance of 10
pixels (10um) in scleral spur localization, while in the task of angle closure
classification, all the algorithms achieved satisfactory performances, with two
best obtaining an accuracy rate of 100%.
