DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images

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. [Display omitted] •Provides the DRAC dataset, top-performing methods, and results•Presents the deep learning methods in DR grading and lesion segmentation•Summarizes the strategies for improving the model performance Diabetic retinopathy (DR) is a common eye disease that can lead to visual impairment and even blindness. The study of DR is an important area that significantly affects the lives of millions of people worldwide. Understanding and managing DR is not only a medical challenge but also a societal one, emphasizing the need for early detection and intervention. A key to such understanding is ultra-wide optical coherence tomography angiography (UW-OCTA), a non-invasive imaging modality that could enable precise assessment of microvascular changes in retinal layers. To this end, we organized a medical image challenge and provided a UW-OCTA dataset for developing the computer-aided diagnostic system for DR diagnosis. The dataset can potentially accelerate the development of advanced artificial intelligence technologies and ultimately improve patient care. The DRAC challenge explored the use of artificial intelligence to tackle clinical tasks related to diabetic retinopathy (DR) using ultra-wide OCTA imaging. Here, the organizers present a comprehensive summary of the top three algorithms and the results for each task, including image quali