Awesome

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

2. Train a model by:

python CycleGAN.py

or

python UNIT.py

3. 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).<br/>(An updated image normalization, present in the current version of this repo, has fixed the intensity error seen in these results.)

CycleGAN - T1 to T2

CycleGAN - T2 to T1

UNIT - T1 to T2

UNIT - T2 to T1