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A Multi-task Network to Detect Junctions in Retinal Vasculature

  • Fatmatülzehra UsluEmail author
  • Anil Anthony Bharath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Junctions in the retinal vasculature are key points to be able to extract its topology, but they vary in appearance, depending on vessel density, width and branching/crossing angles. The complexity of junction patterns is usually accompanied by a scarcity of labels, which discourages the usage of very deep networks for their detection. We propose a multi-task network, generating labels for vessel interior, centerline, edges and junction patterns, to provide additional information to facilitate junction detection. After the initial detection of potential junctions in junction-selective probability maps, candidate locations are re-examined in centerline probability maps to verify if they connect at least 3 branches. The experiments on the DRIVE and IOSTAR showed that our method outperformed a recent study in which a popular deep network was trained as a classifier to find junctions. Moreover, the proposed approach is applicable to unseen datasets with the same degree of success, after training it only once.

Keywords

Restricted Boltzmann machines Deep networks Bifurcation Crossing Fundus images 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.BICV Group, Bioengineering DepartmentImperial College LondonLondonUK

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