CycleGAN

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CycleGAN

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# A Keras CycleGAN for nifti data  
We provide a Keras implementation for unpaired and image-to-image translation, i.e. CycleGAN [1], for nifti data.

We proposed an application of CycleGAN to generate synthetic diffusion MRI scalar maps from structural T1-weighted images, see our paper [2].

![T1 to FA/MD](https://github.com/xuagu37/CycleGAN/blob/master/images/T1_FA_MD.jpg)

## Getting started
### Prepare training data  
We prepare the training data by stacking subjects on the fourth dimention.  
For example, we extract 1 slice from each subject for 1000 subjects and then stack all slices to the fourth dimention.  
The created training data will have the size of [X, Y, 1, 1000].  
![T1_subject_1_1000_slice_66](https://github.com/xuagu37/CycleGAN/blob/master/images/T1_subject_1_1000_slice_66.png)

### Training  
```python
# Create a CycleGAN on GPU 0 
myCycleGAN = CycleGAN(0)

# Set directories  
trainA_dir = '/home/xuagu37/CycleGAN/data/T1_training.nii.gz'  
trainB_dir = '/home/xuagu37/CycleGAN/data/FA_training.nii.gz'  
models_dir = '/home/xuagu37/CycleGAN/train_T1_FA/models'  
output_sample_dir = '/home/xuagu37/CycleGAN/train_T1_FA/output_sample.png'

# Set training parameters  
batch_size = 10  
epochs = 200  
normalization_factor_A = 1000  
normalization_factor_B = 1

# Start training  
myCycleGAN.train(trainA_dir, normalization_factor_A, trainB_dir, normalization_factor_B, models_dir, batch_size, epochs, output_sample_dir=output_sample_dir, output_sample_channels=1)
```

### Synthesize  
```python
# Set directory to the trained model  
G_X2Y_dir = '/home/xuagu37/CycleGAN/train_T1_FA/models/G_A2B_weights_epoch_100.hdf5'

# Set directory to the test data  
test_X_dir = '/home/xuagu37/CycleGAN/data/T1_test.nii.gz'

# Set directory to save the synthetic data  
synthetic_Y_dir ='/home/xuagu37/CycleGAN/train_T1_FA/synthetic/FA_synthetic.nii.gz'

# Synthesize
normalization_factor_X = 1000   
normalization_factor_Y = 1  
myCycleGAN.synthesize(G_X2Y_dir, test_X_dir, normalization_factor_X, synthetic_Y_dir, normalization_factor_Y)  
```

## References
[1] Zhu, J.Y., Park, T., Isola, P. and Efros, A.A., 2017. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2242-2251). IEEE.  
[2] Gu, X., Knutsson, H., Nilsson, M. and Eklund, A., 2019. Generating diffusion MRI scalar maps from T1 weighted images using generative adversarial networks. In Scandinavian Conference on Image Analysis (pp. 489-498). Springer, Cham.

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