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Junctions in the retinal vasculature are key points to be able to extract itstopology, but they vary in appearance, depending on vessel density, width andbranching/crossing angles. The complexity of junction patterns is usuallyaccompanied by a scarcity of labels, which discourages the usage of very deepnetworks for their detection. We propose a multi-task network, generatinglabels for vessel interior, centerline, edges and junction patterns, to provideadditional information to facilitate junction detection. After the initialdetection of potential junctions in junction-selective probability maps,candidate locations are re-examined in centerline probability maps to verify ifthey connect at least 3 branches. The experiments on the DRIVE and IOSTARshowed that our method outperformed a recent study in which a popular deepnetwork was trained as a classifier to find junctions. Moreover, the proposedapproach is applicable to unseen datasets with the same degree of success,after training it only once.

A Multi-task Network to Detect Junctions in Retinal Vasculature