MICCAI2021 RVGAN

This code is for our paper "RV-GAN: Segmenting Retinal Vascular Structure inFundus Photographs using a Novel Multi-scaleGenerative Adversarial Network" which is part of the supplementary materials for MICCAI 2021 conference. The paper has since been accpeted to MICCAI 2021 and will be presented in September 2021.

Arxiv Pre-print

https://arxiv.org/pdf/2101.00535v2.pdf

Springer

https://link.springer.com/chapter/10.1007/978-3-030-87237-3_4

Citation

@inproceedings{kamran2021rv,
  title={RV-GAN: Segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network},
  author={Kamran, Sharif Amit and Hossain, Khondker Fariha and Tavakkoli, Alireza and Zuckerbrod, Stewart Lee and Sanders, Kenton M and Baker, Salah A},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={34--44},
  year={2021},
  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==2.0.0
sudo pip3 install keras==2.3.1

• Install packages from requirements.txt

sudo pip3 install -r requirements.txt

DRIVE Dataset

• Please cite the paper if you use their data

@article{staal2004ridge,
  title={Ridge-based vessel segmentation in color images of the retina},
  author={Staal, Joes and Abr{\`a}moff, Michael D and Niemeijer, Meindert and Viergever, Max A and Van Ginneken, Bram},
  journal={IEEE transactions on medical imaging},
  volume={23},
  number={4},
  pages={501--509},
  year={2004},
  publisher={IEEE}
}

Dataset download link for DRIVE

https://drive.grand-challenge.org/

STARE Dataset

• Please cite the paper if you use their data

@article{hoover2000locating,
  title={Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response},
  author={Hoover, AD and Kouznetsova, Valentina and Goldbaum, Michael},
  journal={IEEE Transactions on Medical imaging},
  volume={19},
  number={3},
  pages={203--210},
  year={2000},
  publisher={IEEE}
}

Dataset download link for STARE

https://cecas.clemson.edu/~ahoover/stare/

CHASE-DB1 Dataset

• Please cite the paper if you use their data

@article{owen2009measuring,
  title={Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program},
  author={Owen, Christopher G and Rudnicka, Alicja R and Mullen, Robert and Barman, Sarah A and Monekosso, Dorothy and Whincup, Peter H and Ng, Jeffrey and Paterson, Carl},
  journal={Investigative ophthalmology \& visual science},
  volume={50},
  number={5},
  pages={2004--2010},
  year={2009},
  publisher={The Association for Research in Vision and Ophthalmology}
}

Dataset download link for CHASE-DB1

https://blogs.kingston.ac.uk/retinal/chasedb1/

Dataset Pre-processing

• Type this in terminal to run the strided_crop_DRIVE.py, strided_crop_STARE.py or strided_crop_CHASE.py file.

python3 strided_crop_DRIVE.py --input_dim=128 --stride=32

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

    '--input_dim', type=int, default=128
    '--stride', type=int, default=32

NPZ file conversion

• Convert all the images to npz format using convert_npz_DRIVE.py, convert_npz_STARE.py or convert_npz_CHASE.py file.

python3 convert_npz_DRIVE.py --input_dim=(128,128) --n_crops=210 --outfile_name='DRIVE'

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

    '--input_dim', type=int, default=(128,128)
    '--n_crops', type=int, default=210
    '--outfile_name', type=str, default='DRIVE'

Training

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

python3 train.py --npz_file=DRIVE --batch=4 --epochs=200 --savedir=RVGAN --resume_training=no --inner_weight=0.5

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

   '--npz_file', type=str, default='DRIVE.npz', help='path/to/npz/file'
   '--batch_size', type=int, default=24
   '--input_dim', type=int, default=128
   '--epochs', type=int, default=200
   '--savedir', type=str, required=False, help='path/to/save_directory',default='RVGAN'
   '--resume_training', type=str, required=False,  default='no', choices=['yes','no']
   '--inner_weight', type=float, default=0.5

Pretrained Weights

https://drive.google.com/drive/folders/1e_dMNjwsPo9ykoYuy3Fn0cmTg1FH8K5H?usp=sharing

Inference

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

python3 infer.py --test_data=DRIVE --out_dir=test --weight_name_global=global_model_100.h5 --weight_name_local=local_model_100.h5 --stride=3 

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

    '--test_data', type=str, default='DRIVE', required=True, choices=['DRIVE','CHASE','STARE']
    '--out_dir', type=str, default='pred', required=False)
    '--weight_name_global',type=str, help='path/to/global/weight/.h5 file', required=True
    '--weight_name_local',type=str, help='path/to/local/weight/.h5 file', required=True
    '--stride', type=int, default=3, help='For faster inference use stride 16/32, for better result use stride 3.'

License

The code is released under the BSD 3-Clause License, you can read the license file included in the repository for details.