/WGAN-VGG

Pytorch Implementation of Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

Primary LanguagePython

WGAN_VGG [DEPRECATED]

Implementation of Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss https://arxiv.org/abs/1708.00961

DATASET

The 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge by Mayo Clinic (I can't share this data, you should ask at the URL below if you want)
https://www.aapm.org/GrandChallenge/LowDoseCT/

The data_path should look like:

data_path
├── L067
│   ├── quarter_3mm
│   │       ├── L067_QD_3_1.CT.0004.0001 ~ .IMA
│   │       ├── L067_QD_3_1.CT.0004.0002 ~ .IMA
│   │       └── ...
│   └── full_3mm
│           ├── L067_FD_3_1.CT.0004.0001 ~ .IMA
│           ├── L067_FD_3_1.CT.0004.0002 ~ .IMA
│           └── ...
├── L096
│   ├── quarter_3mm
│   │       └── ...
│   └── full_3mm
│           └── ...      
...
│
└── L506
    ├── quarter_3mm
    │       └── ...
    └── full_3mm
            └── ...

Use

Check the arguments.

  1. run python prep.py to convert 'dicom file' to 'numpy array'
  2. run python main.py --load_mode=0 to training. If the available memory(RAM) is more than 10GB, it is faster to run --load_mode=1.
  3. run python main.py --mode='test' --test_iters=*** to test.