The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation

• URL: http://arxiv.org/abs/2011.12643v1
• Date: Wed, 25 Nov 2020 11:10:37 GMT
• Title: The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
• Authors: Bj\"orn Browatzki, J\"orn-Philipp Lies, Christian Wallraven
• Abstract summary: We propose an encoder-decoder framework for the segmentation of blood vessels in retinal images. Experiments on three fundus image datasets demonstrate that this approach achieves state-of-the-art results. We show that this framework - called VLight - avoids overfitting to specific training images and generalizes well across different datasets.
• Score: 3.351714665243138
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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