3D RVGAN

This code is modified version of the official "RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scaleGenerative Adversarial Network" which is part of the supplementary materials for MICCAI 2021 conference. This version dedicated for Brain Tumor Segmentation from 3D images.

RVGAN Arxiv Pre-print

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

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

Dataset download link for DRIVE

https://figshare.com/articles/dataset/brain_tumor_dataset/1512427

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=(64,64,64) --outfile_name='DRIVE'

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

    '--input_dim', type=int, default=(64,64,64)
    '--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=64
   '--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

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.'

Evaluation on test set

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

python3 eval.py --test_data=DRIVE --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']
    '--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.'