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Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias

arXiv 2210.16053 ↗First posted 28 Oct 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 28 Oct 2022.

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12 of 21 references.

References · 12

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