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

HUAZHU FU; FEI LI; XU SUN; XINGXING CAO; JINGAN LIAO; JOSÉ IGNACIO ORLANDO; XING TAO; HRVOJE BOGUNOVIC; XIULAN ZHANG; YUEXIANG LI; SHIHAO ZHANG; MINGKUI TAN; CHENGLANG YUAN; CHENG BIAN; RUITAO XIE; JIONGCHENG LI; XIAOMENG LI; JING WANG; LE GENG; PANMING LI; HUAYING HAO; JIANG LIU; YAN KONG; YONGYONG REN; YANWU XU

MEDICAL IMAGE ANALYSIS

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2020

1361-8415

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 opticneuropathy 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 analysisalgorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there isno 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 from199 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 participatedin the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding resultsfor the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach hadan average Euclidean Distance of 10 pixels (10µm) in scleral spur localization, while in the task of angle closure classification, allthe algorithms achieved satisfactory performances, with two best obtaining an accuracy rate of 100%. These artificial intelligencetechniques have the potential to promote new developments in AS-OCT image analysis and image-based angle closure glaucomaassessment in particular.