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2020 | OriginalPaper | Chapter

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

Authors : Björn Browatzki, Jörn-Philipp Lies, Christian Wallraven

Published in: Ophthalmic Medical Image Analysis

Publisher: Springer International Publishing

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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.

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previous chapter DeSupGAN: Multi-scale Feature Averaging Generative Adversarial Network for Simultaneous De-blurring and Super-Resolution of Retinal Fundus Images

next chapter Retinal Image Quality Assessment via Specific Structures Segmentation

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Title: Encoder-Decoder Networks for Retinal Vessel Segmentation Using Large Multi-scale Patches
Authors: Björn Browatzki
Jörn-Philipp Lies
Christian Wallraven
Publisher: Springer International Publishing
Book: Ophthalmic Medical Image Analysis
Print ISBN: 978-3-030-63418-6

Electronic ISBN: 978-3-030-63419-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-63419-3_5

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