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
CountryPeru
CityLima
Period20/10/820/10/8

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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