Iterative Deep Retinal Topology Extraction

This paper tackles the task of estimating the topology of filamentary networks such as retinal vessels. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity between the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the filamentary network, inspired by a human delineating a complex network with the tip of their finger. We perform a qualitative and quantitative evaluation on retinal veins and arteries topology extraction on DRIVE dataset, where we show superior performance to very strong baselines.

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