Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias
This work addresses early diagnosis of diabetic retinopathy for medical imaging, but it is incremental as it matches rather than surpasses existing methods.
The paper tackles automated diabetic retinopathy grading using OCTA images and vessel segmentation maps, achieving performance equal to the baseline model on the DRAC challenge.
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.