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Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT

[Arxiv paper]

Code usage

  1. Prepare your dataset under the directory 'data' in the CycleGAN or UNIT folder and set dataset name to parameter 'image_folder' in model init function.
  • Directory structure on new dataset needed for training and testing:
    • data/Dataset-name/trainA
    • data/Dataset-name/trainB
    • data/Dataset-name/testA
    • data/Dataset-name/testB
  1. Train a model by:
python CycleGAN.py

or

python UNIT.py
  1. Generate synthetic images by following specifications under:
  • CycleGAN/generate_images/ReadMe.md
  • UNIT/generate_images/ReadMe.md

Result GIFs - 304x256 pixel images

Left: Input image. Middle: Synthetic images generated during training. Right: Ground truth.
Histograms show pixel value distributions for synthetic images (blue) compared to ground truth (brown).

CycleGAN - T1 to T2

CycleGAN - T2 to T1

UNIT - T1 to T2

UNIT - T2 to T1