  With the rapid development of artificial intelligence (AI) in medical image
processing, deep learning in color fundus photography (CFP) analysis is also
evolving. Although there are some open-source, labeled datasets of CFPs in the
ophthalmology community, large-scale datasets for screening only have labels of
disease categories, and datasets with annotations of fundus structures are
usually small in size. In addition, labeling standards are not uniform across
datasets, and there is no clear information on the acquisition device. Here we
release a multi-annotation, multi-quality, and multi-device color fundus image
dataset for glaucoma analysis on an original challenge -- Retinal Fundus
Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000
color fundus images with annotations of glaucoma classification, optic disc/cup
segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge
sets three sub-tasks of automatic glaucoma diagnosis and fundus structure
analysis and provides an online evaluation framework. Based on the
characteristics of multi-device and multi-quality data, some methods with
strong generalizations are provided in the challenge to make the predictions
more robust. This shows that REFUGE2 brings attention to the characteristics of
real-world multi-domain data, bridging the gap between scientific research and
clinical application.
