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

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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)