Deep learning for semantic segmentation of 3D vasculature in diabetic retinopathy and healthy control participants enabling evaluation of vessel metrics
Poster presented as part of the Crick BioImage Analysis Symposium.
Optical coherence tomography angiography (OCT-A) is an imaging modality enabling 3D imaging of the human eye retinal vasculature in vivo. Classification of arteries and veins in retinal images is of high medical interest as these systems are divergently affected in many retinal vascular diseases that affect vision . Arteries and veins are more challenging to distinguish in 3D OCT-A images than via 2D retinal photography . Here, a 2-stage deep learning approach is presented for classification of arteries and veins in 3D. This enables the evaluation of vessel metrics in diabetic retinopathy and healthy control participant datasets.
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