Encoder-Decoder Networks for Retinal Vessel Segmentation Using Large Multi-scale Patches

Björn Browatzki, Jörn Philipp Lies, Christian Wallraven

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images that relies on the extraction of large-scale patches at multiple image-scales during training. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results and can be implemented using a simple and efficient fully-convolutional network with a parameter count of less than 0.8M. Furthermore, we show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets, which makes it highly suitable for real-world applications where robustness, accuracy as well as low inference time on high-resolution fundus images is required.

    Original languageEnglish
    Title of host publicationOphthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings
    EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages42-52
    Number of pages11
    ISBN (Print)9783030634186
    DOIs
    Publication statusPublished - 2020
    Event6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
    Duration: 2020 Oct 82020 Oct 8

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12069 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference6th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2020, held in conjunction with 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020
    Country/TerritoryPeru
    CityLima
    Period20/10/820/10/8

    Bibliographical note

    Publisher Copyright:
    © 2020, Springer Nature Switzerland AG.

    Keywords

    • Fundus image
    • Residual networks
    • Retinal vessel detection
    • Semantic segmentation

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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