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2018 | OriginalPaper | Chapter

A Multi-task Network to Detect Junctions in Retinal Vasculature

Authors : Fatmatülzehra Uslu, Anil Anthony Bharath

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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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.

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previous chapter Deep Supervision with Additional Labels for Retinal Vessel Segmentation Task

next chapter A Multitask Learning Architecture for Simultaneous Segmentation of Bright and Red Lesions in Fundus Images

The ground truth data is available at http://retinacheck.org/datasets.

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Title: A Multi-task Network to Detect Junctions in Retinal Vasculature
Authors: Fatmatülzehra Uslu
Anil Anthony Bharath
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00933-5

Electronic ISBN: 978-3-030-00934-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00934-2_11

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