SuperRetina for Retinal Image Matching

This is the official source code of our ECCV2022 paper: Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching.

Environment

We used Anaconda to setup a deep learning workspace that supports PyTorch. Run the following script to install all the required packages.

conda create -n SuperRetina python==3.8 -y
conda activate SuperRetina
git clone https://github.com/ruc-aimc-lab/SuperRetina.git
cd SuperRetina
pip install -r requirements.txt

Downloads

Data

See the data pape. SuperRetina is trained on a small amount of keypoint annotations, which can be either manually labeled or auto-labeled by a specific keypoint detection algorithm. Check notebooks/read_keypoint_labels.ipynb to see our data format of keypoint annotations.

Models

You may skip the training stage and use our provided models for keypoint detection and description on retinal images.

Google drive

Put the trained model into save/ folder.

Code

Training

Write the config/train.yaml file before training SuperRetina. Here we provide a demo training config file. Then you can train SuperRetina on your own data by using the following command.

python train.py

Inference

Registration Performance

The test_on_FIRE.py code shows how image registration is performed on the FIRE dataset.

python test_on_FIRE.py

If everything goes well, you shall see the following message on your screen:

----------------------------------------
Failed:0.00%, Inaccurate:1.50%, Acceptable:98.50%
----------------------------------------
S: 0.950, P: 0.554, A: 0.783, mAUC: 0.762

Identity Verification Performance

The test_on_VARIA.py code shows how identity verification is performed on the VARIA dataset.

python test_on_VARIA.py

If everything goes well, you shall see the following message on your screen:

VARIA DATASET
EER: 0.00%, threshold: 40

We have also provided some tutorial codes showing step-by-step usage of SuperRetina:

notebooks/tutorial-inference.ipynb: Perform registration for a given pair of images.
notebooks/eval-registration-on-FIRE.ipynb: Evaluation on the FIRE dataset.

Citations

If you find this repository useful, please consider citing:

@inproceedings{liu2022SuperRetina,
  title={Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching},
  author={Jiazhen Liu and Xirong Li and Qijie Wei and Jie Xu and Dayong Ding},
  booktitle={Proceedings of the 17th European Conference on Computer Vision (ECCV)},
  year={2022}
}

Contact

If you encounter any issue when running the code, please feel free to reach us either by creating a new issue in the GitHub or by emailing

Jiazhen Liu (liujiazhen@ruc.edu.cn)

bailianfa/SuperRetina