Encoder-Decoder Networks for Retinal Vessel Segmentation Using Large Multi-scale Patches
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.
Type
Publication
6th 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
