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.
@article{arxiv.2011.12643,
title = {The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation},
author = {Björn Browatzki and Jörn-Philipp Lies and Christian Wallraven},
journal= {arXiv preprint arXiv:2011.12643},
year = {2020}
}