Pytorch implementation of vox2vox, a 3D volume-to-volume generative adversarial network proposed by M. Cirillo, D. Abramian and A. Eklund

• Windows/ Linux/ Mac
• torch>=0.4.0
• torchvision
• matplotlib
• numpy, scipy
• PIL
• scikit-image
• pyvista (for visulization)
• h5py

All volumes should be saved as h5 files since the dataloader uses h5py to load and create training batches.

Datasets should be split into two folders train and test, in which they should be placed under the same folder in /data. Each of the train and test folder should have image and segmentation volumes with the same file name. Suggested extension for image volume is .im and .seg for segmentation volume.

Clone this repo by: git clone https://github.com/chinokenochkan/vox2vox

Run training job: cd vox2vox && python train.py --dataset <your dataset name>

Here are some of the possible parameters you can modify during training:

• --n_epochs: number of epochs to train for
• --glr: generator learning rate for Adam optimizer
• --dlr: discriminator learning rate for Adam optimizer
• --dataset_name: name of the dataset you are using, should be consistent with the name of the dataset folder in /data
• --batch_size: training batch size, default is 1
• --image_height/ --image_width/ --image_depth: image dimensions h/w/d
• d_threshold: accuracy threshold in which the discriminator will be trained as long as its accuracy is below it

Thanks @eriklindernoren for the great starter repo

Oringal paper: M. Cirillo, D. Abramian and A. Eklund