rafidrm / gancer

Generative Adversarial Networks for Computationally Efficient Radiotherapy

GANCER: Generative Adversarial Nets for Computationally Efficient Radiotherapy

Code for the paper "Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks", submitted to Machine Learning in Health Care 2018.

Knowledge-based planning (KBP) is an automated approach to radiation therapy treatment planning that involves first predicting a desirable treatment plan, before correcting it to a deliverable one. In this work, we propose a GAN approach to predicting desirable 3D dose distributions.

This code contains the implementation specifically for the GAN. We will provide the code for the optimization in a later update. Note that the dataset used in the original paper cannot be shared publicly. This will be resolved in a later update, where we will provide a synthetic dataset. Alternatively, you can use a public dataset, such as CORT, as long as you modify the dataloader appropriately.

Prerequisites

• Linux or OS X
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Getting started

Installation

• Setup pipenv virtual environment and enter it

pipenv install --dev --three
pipenv shell

• Install PyTorch and dependencies from http://pytorch.org
• Install Torch vision from the source.

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

• Install python libraries visdom and dominate.

pip install visdom
pip install dominate
pip install opencv-python

• Add checkpoints and datasets directories.

cd cancerGAN/
mkdir checkpoints
mkdir datasets
mkdir results

• Training on cancer dataset:

python train.py --dataroot ./datasets/cancer --name cancer_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction AtoB --lambda_A 100 --dataset_mode slice --no_lsgan --norm batch --pool_size 0 --loadSize 256 --print_freq 200 --batchSize 16 --gpu_ids 0

• Testing on cancer dataset:

python test.py --dataroot ./datasets/cancer --name cancer_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction AtoB --dataset_mode slice --norm batch --loadSize 256 --phase test --how_many 99999

• Training 3d cancer dataset:

python train.py --dataroot ./datasets/voxels --name vox2vox --model vox2vox --which_model_netG unet_64_3d --which_model_netD n_layers_3d --which_direction AtoB --dataset_mode voxel --norm batch_3d --loadSize 64 --fineSize 64 --print_freq 100 --gpu_ids 0