Retina-VesselNet
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A Simple U-net model for Retinal Blood Vessel Segmentation based on tensorflow2
[NOTE] Since this project has upgraded to Tensorflow 2.3 on 18th March 2021, you can find old branches which have stopped maintenance from:
- [2019-6-9] keras-tensorflow branch: https://github.com/DeepTrial/Retina-VesselNet/tree/keras-tensorflow-1.X
- [2018-5-2] keras-theano branch: https://github.com/DeepTrial/Retina-VesselNet/tree/keras-theano
VesselNet
A Simple U-net model for Retinal Blood Vessel Segmentation with DRIVE dataset
Project Structure
We provide 2 version of projects: jupyter notebook and .py file
. The implementation of these two versions is completely consistent. Choose one version and enjoy it!
First to run
For the first time, I recommand to use the version of jupyter notebook, it will give you an intuitive presentation. Different notebooks are made for different purpose:
-
EntireBookForColab.ipynb
contains complete part of projects such as process, train, test. Furthermore, it can be run on Google Colab -
PreprocessIllustartion.ipynb
shows some preprocess methods for retinal images. -
TestAndEvaluation.ipynb
is the part for evaluating and testing the model. -
Training.ipynb
is the part for defining and training the model.
Remenber to modify the dataset path according to your setting.
Pretrained Model
Train/Test your own image
If you want to test your own image, put your image to the the relevant dir and adjust the patch_size
,stride
according to your image size.
Citation
This project has been used in:
@inproceedings{2020Eye3DVas,
title={Eye3DVas: three-dimensional reconstruction of retinal vascular structures by integrating fundus image features},
author={ Yao Z. and He K. and Zhou H. and Zhang Z. and Xing C. and Zhou F.},
booktitle={Frontiers in Optics},
year={2020},
}
Reference
This project is based on the following 2 papers:
U-Net: Convolutional Networks for Biomedical Image Segmentation
Densely Connected Convolutional Networks