/RSAN

RSAN: Residual Spatial Attention Network for Retinal Vessel Segmentation (ICONIP 2020)

Primary LanguagePython

RSAN

Residual Spatial Attention Network for Retinal Vessel Segmentation(ICONIP2020)

This code is for the paper: Residual Spatial Attention Network for Retinal Vessel Segmentation. We report state-of-the-art performances on DRIVE and CHASE DB1 datasets.

Code written by Changlu Guo, Budapest University of Technology and Economics(BME).

We train and evaluate on Ubuntu 16.04, it will also work for Windows and OS.

Quick start

Train: Run train_drive.py or train_chase.py

Test: Run eval_drive.py or eval_chase.py

Results

Results Row 1 is for DRIVE dataset. Row 2 is for CHASE DB1 dataset. (a) Color fundus images, (b) segmentation results of Backbone, (c) segmentation results of Backbone+DropBlock, (d) segmentation results of RSAN, (e) corresponding ground truths.

Environments

Keras 2.3.1
Tensorflow==1.14.0

If you are inspired by our work, please cite this paper.

@misc{guo2020residual,
title={Residual Spatial Attention Network for Retinal Vessel Segmentation},
author={Changlu Guo and Márton Szemenyei and Yugen Yi and Wei Zhou and Haodong Bian},
year={2020},
eprint={2009.08829},
archivePrefix={arXiv},
primaryClass={eess.IV}
}