Fully Automated Tree Topology Estimation and Artery-Vein Classification
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We present a fully automatic technique for extracting the retinal vascular topology, i.e., how the different vessels are connected to each other, given a single color fundus image. Determining this connectivity is very challenging because vessels cross each other in a 2D image, obscuring their true paths. We validated the usefulness of our extraction method by using it to achieve state-of-the-art results in retinal artery-vein classification. Our proposed approach works as follows. We first segment the retinal vessels using our previously developed state-of-the-art segmentation method. Then, we estimate an initial graph from the extracted vessels and assign the most likely blood flow to each edge. We then use a handful of high-level operations (HLOs) to fix errors in the graph. These HLOs include detaching neighboring nodes, shifting the endpoints of an edge, and reversing the estimated blood flow direction for a branch. We use a novel cost function to find the optimal set of HLO operations for a given graph. Finally, we show that our extracted vascular structure is correct by propagating artery/vein labels along the branches. As our experiments show, our topology-based artery-vein labeling achieved state-of-the-art results on multiple datasets. We also performed several ablation studies to verify the importance of the different components of our proposed method.
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