  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.
