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.

SharifAmit/Fundus2Angio