  Recent studies suggest that early stages of diabetic retinopathy (DR) can be
diagnosed by monitoring vascular changes in the deep vascular complex. In this
work, we investigate a novel method for automated DR grading based on optical
coherence tomography angiography (OCTA) images. Our work combines OCTA scans
with their vessel segmentations, which then serve as inputs to task specific
networks for lesion segmentation, image quality assessment and DR grading. For
this, we generate synthetic OCTA images to train a segmentation network that
can be directly applied on real OCTA data. We test our approach on MICCAI
2022's DR analysis challenge (DRAC). In our experiments, the proposed method
performs equally well as the baseline model.
