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Code repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'

License: GNU General Public License v3.0

Python 97.28% Shell 0.04% HTML 2.16% CSS 0.51%

gan-mri's Introduction

采用的思路与biomedicalimaging比较用老版本的tensorflow 1.2.2/2.0.0。 用conda与pip管理软件版本

软件需要人们维护,不然无法使用。

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

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).
(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

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