Fundus2Video

This repository contains the official implementation of our paper:
Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance
Published at MICCAI 2024.
You can find the paper here.

Dependencies

This code is built upon the pix2pixHD framework developed by NVIDIA, which can be found at pix2pixHD GitHub repository.

Install the required dependencies using:

pip install -r requirements.txt

Dataset and Weights

Due to data privacy concerns, we are unable to provide the dataset and pre-trained weights used in this study.

However, you can adapt the code to your own dataset by modifying the file:
data/aligned_dataset_fundus2video_tempo.py

Make sure your dataset follows a structure compatible with the expected format in the code.

Training

Run the following command to train the model:

python train.py

If you find this work useful, please cite our paper:

@inproceedings{zhang2024fundus2video,
  title={Fundus2Video: Cross-Modal Angiography Video Generation from Static Fundus Photography with Clinical Knowledge Guidance},
  author={Zhang, Weiyi and Huang, Siyu and Yang, Jiancheng and Chen, Ruoyu and Ge, Zongyuan and Zheng, Yingfeng and Shi, Danli and He, Mingguang},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={689--699},
  year={2024},
  organization={Springer}
}