Qian Bo, Chen Hao, Wang Xiangning, Guan Zhouyu, Li Tingyao, Jin Yixiao, Wu Yilan, Wen Yang, Che Haoxuan, Kwon Gitaek, Kim Jaeyoung, Choi Sungjin, Shin Seoyoung, Krause Felix, Unterdechler Markus, Hou Junlin, Feng Rui, Li Yihao, El Habib Daho Mostafa, Yang Dawei, Wu Qiang, Zhang Ping, Yang Xiaokang, Cai Yiyu, Tan Gavin Siew Wei, Cheung Carol Y, Jia Weiping, Li Huating, Tham Yih Chung, Wong Tien Yin, Sheng Bin
Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Patterns (N Y). 2024 Feb 8;5(3):100929. doi: 10.1016/j.patter.2024.100929. eCollection 2024 Mar 8.
We described 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). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.
我们在第25届医学图像计算与计算机辅助干预国际会议（MICCAI 2022）期间介绍了一项名为“DRAC - 糖尿病视网膜病变分析挑战赛”的挑战。在该挑战赛中，我们提供了DRAC数据集，这是一个超广角光学相干断层扫描血管造影（UW - OCTA）数据集（包含1103张图像），涉及三项主要临床任务：糖尿病视网膜病变（DR）病变分割、图像质量评估和DR分级。科学界对该挑战赛反应积极，分别有11个、12个和13个团队针对这三项任务提交了不同的解决方案。本文简要总结并分析了所有挑战任务中表现最佳的解决方案及结果。这些解决方案可为利用UW - OCTA图像开发用于图像质量评估和DR诊断的准确分类与分割模型提供实用指导，有望提高医疗专业人员的诊断能力。该数据集已发布，以支持用于DR评估的计算机辅助诊断系统的开发。
