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Abstract #105139 Published in IGR 23-2
Automated Retinal Vascular Topological Information Extraction From OCTA
Lee AX; Saxena A; Chua J; Schmetterer L; Tan BAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2022; 2022: 1839-1842
The retinal vascular system adapts and reacts rapidly to ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. Here we present a combination of methods to further extract vascular information from [Formula: see text] wide-field optical coherence tomography angiography (OCTA). An integrated U-Net for the segmentation and classification of arteries and veins reached a segmentation IoU of 0.7095±0.0224, and classification IoU of 0.8793±0.1049 and 0.8928±0.0929 respectively. A correcting algorithm which uses topological information was created to correct the misclassification and connectivity of the vessels, which showed an average increase of 8.29% in IoU. Finally, the vessel morphometry of branch orders was extracted, where this allows the direct comparison of artery/vein, arterioles/venules and capillaries.
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