  Optical coherence tomography angiography (OCTA) is a novel imaging modality
that has been widely utilized in ophthalmology and neuroscience studies to
observe retinal vessels and microvascular systems. However, publicly available
OCTA datasets remain scarce. In this paper, we introduce the largest and most
comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under
two fields of view (FOVs) from 500 subjects. The dataset provides rich images
and annotations including two modalities (OCT/OCTA volumes), six types of
projections, four types of text labels (age / gender / eye / disease) and seven
types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D
FAZ/retinal layers). Then, we propose a multi-object segmentation task called
CAVF, which integrates capillary segmentation, artery segmentation, vein
segmentation, and FAZ segmentation under a unified framework. In addition, we
optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one
of the segmentation baselines. Experimental results demonstrate that IPN-V2
achieves an ~10% mIoU improvement over IPN on CAVF task. Finally, we further
study the impact of several dataset characteristics: the training set size, the
model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the
diseases. The dataset and code are publicly available at:
https://ieee-dataport.org/open-access/octa-500.
