Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments.

翻译：计算机辅助的糖尿病视网膜病变（DR）自动分析对于降低视力损伤乃至失明风险具有重要意义。超广角光学相干断层扫描血管造影（UW-OCTA）作为一种无创、安全的成像模态，在DR诊断系统中具有重要价值，但目前仍缺乏用于模型开发与评估的公开基准数据集。为促进基于UW-OCTA图像的糖尿病视网膜病变分析研究及科学基准测试，我们于第25届医学图像计算与计算机辅助介入国际会议（MICCAI 2022）期间举办了名为"DRAC——糖尿病视网膜病变分析挑战赛"的竞赛。该挑战包含三项任务：DR病灶分割、图像质量评估与DR分级。学界对此挑战反响积极，分别有来自不同地域研究机构的11支、12支及13支参赛队伍针对这三项任务提交了差异化解决方案。本文对各任务最优解决方案及结果进行了总结分析。顶尖算法结果表明，数据增强、模型架构设计及网络集成策略对提升深度学习模型性能具有关键作用。这些发现有望推动糖尿病视网膜病变分析领域的新进展。本挑战赛将持续开放注册与提交，为未来方法学发展提供基准测试平台。