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:计算机辅助自动分析糖尿病视网膜病变(DR)对于降低视觉损失和失明的风险至关重要。全视野光学相干断层扫描血管影像(UW-OCTA)是一种非侵入性和安全的DR诊断系统成像方式,但是缺乏公开的标准数据集来推动模型的开发和评估。为了促进进一步的研究和使用UW-OCTA图像分析糖尿病视网膜病变的科学基准,我们在第25届医学图像计算和计算机辅助干预国际会议(MICCAI 2022)上举办了一个名为"DRAC - Diabetic Retinopathy Analysis Challenge"的比赛。 挑战包括三个任务:DR病变分割、图像质量评估和DR分级。来自全球不同机构的11、12和13个队伍积极响应了挑战,并提交了不同的解决方案。本文总结和分析了挑战的各个任务的最佳表现解决方案和结果。顶尖算法的结果表明,数据增强、模型架构和网络集成在提高深度学习模型的性能方面具有重要意义。这些发现有可能推动糖尿病视网膜病变分析的新发展。该挑战对于注册和提交以供未来方法学发展基准测试仍然是开放的。