AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
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DOI：
10.1016/j.media.2020.101798
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发表时间：
2020-12-01
影响因子：
10.9
通讯作者：
Zhou, Rouxi
中科院分区：
文献类型：
--
作者：
Fu, Huazhu;Li, Fei;Zhou, Rouxi
闭角型青光眼（ACG）是一种比开角型青光眼更具侵袭性的疾病，其前房角（ACA）解剖结构异常可引起眼压升高，逐渐导致青光眼视神经病变，最终导致视力损害和失明。前段光学相干层析成像（AS-OCT）提供了一种快速、非接触的方法来区分闭合角和开角。虽然已经开发了许多用于青光眼诊断的医学图像分析算法，但只有少数研究集中在AS-OCT成像上。特别是，没有公共的AS-OCT数据集可用于以统一的方式评估现有方法，这限制了自动化角度闭合检测和评估技术的发展。为了解决这个问题，我们组织了闭角型青光眼评估挑战（AGE），与MICCAI 2019一起举行。AGE挑战包括两个任务：巩膜骨刺定位和角闭合分类。为了应对这一挑战，我们发布了来自199名患者的4800张带注释的AS-OCT图像的大型数据集，并提出了一个评估框架，以基准和比较不同的模型。在AGE挑战赛期间，超过200个团队在线注册，并提交了1100多个结果供在线评估。最终，8支队伍参加了现场挑战赛。在本文中，我们总结了这八种现场挑战方法，并分析了它们在两个任务中的对应结果。我们进一步讨论了局限性和未来的发展方向。在AGE挑战中，表现最好的方法在巩膜骨刺定位中的平均欧氏距离为10像素（10 μ m），而在角闭合分类任务中，所有算法都取得了满意的性能，其中两个最好的算法获得了100%的准确率。这些人工智能技术有可能促进AS-OCT图像分析和基于图像的闭角型青光眼评估的新发展。(C) 2020 Elsevier B.V.版权所有
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 (10 mu m) 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%. These artificial intelligence techniques have the potential to promote new developments in AS-OCT image analysis and image-based angle closure glaucoma assessment in particular. (C) 2020 Elsevier B.V. All rights reserved.