ISVC2020 Fundus2Angio

This code is part of the supplementary materials for the ISVC 2020 for our paper Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography . The paper has since been accpeted to ISVC 2020 and will be preseneted in October 2020.

Arxiv Pre-print

https://arxiv.org/abs/2005.05267

Citation

@inproceedings{kamran2020fundus2angio,
  title={Fundus2Angio: A Conditional GAN Architecture for Generating Fluorescein Angiography Images from Retinal Fundus Photography},
  author={Kamran, Sharif Amit and Hossain, Khondker Fariha and Tavakkoli, Alireza and Zuckerbrod, Stewart and Baker, Salah A and Sanders, Kenton M},
  booktitle={International Symposium on Visual Computing},
  pages={125--138},
  year={2020},
  organization={Springer}
}

Virtual Oral Presentation (YouTube)

Pre-requisite

• Ubuntu 18.04 / Windows 7 or later
• NVIDIA Graphics card

Installation Instruction for Ubuntu

• Download and Install Nvidia Drivers
• Download and Install via Runfile Nvidia Cuda Toolkit 10.0
• Download and Install Nvidia CuDNN 7.6.5 or later
• Install Pip3 and Python3 enviornment

sudo apt-get install pip3 python3-dev

• Install Tensorflow-Gpu version-2.0.0 and Keras version-2.3.1

sudo pip3 install tensorflow-gpu==2.0.3
sudo pip3 install keras==2.3.1

• Install packages from requirements.txt

sudo pip3 -r requirements.txt

Dataset download link for Hajeb et al.

https://sites.google.com/site/hosseinrabbanikhorasgani/datasets-1/fundus-fluorescein-angiogram-photographs--colour-fundus-images-of-diabetic-patients

• Please cite the paper if you use their data

@article{hajeb2012diabetic,
  title={Diabetic retinopathy grading by digital curvelet transform},
  author={Hajeb Mohammad Alipour, Shirin and Rabbani, Hossein and Akhlaghi, Mohammad Reza},
  journal={Computational and mathematical methods in medicine},
  volume={2012},
  year={2012},
  publisher={Hindawi}
}

• Folder structure for data pre-processing given below. Please make sure it matches with your local repository.

├── Dataset
|   ├──ABNORMAL
|   ├──NORMAL

Dataset Pre-processing

• Type this in terminal to run the random_crop.py file

python3 random_crop.py --output_dir=data --input_dim=512 --datadir=Dataset

• There are different flags to choose from. Not all of them are mandatory.

    '--input_dim', type=int, default=512
    '--n_crops', type=int, default=50
    '--datadir', type=str, required=True, help='path/to/data_directory',default='Dataset'
    '--output_dir', type=str, default='data'   

NPZ file conversion

• Convert all the images to npz format

python3 convert_npz.py --outfile_name=fundus2angio --input_dim=512 --datadir=data --n_crops=50

• There are different flags to choose from. Not all of them are mandatory.

    '--input_dim', type=int, default=512
    '--n_crops', type=int, default=50
    '--datadir', type=str, required=True, help='path/to/data_directory',default='data'
    '--outfile_name', type=str, default='fundus2angio'
    '--n_images', type=int, default=17

Training

• Type this in terminal to run the train.py file

python3 train.py --npz_file=fundus2angio --batch=4 --epochs=100

• There are different flags to choose from. Not all of them are mandatory

   '--npz_file', type=str, default='fundus2angio', help='path/to/npz/file'
   '--batch_size', type=int, default=4
   '--input_dim', type=int, default=512
   '--epochs', type=int, default=100

License

The code is released under the MIT License, you can read the license file included in the repository for details.