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medicaldiffusion

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Medical Diffusion: This repository contains the code to our paper Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Synthesis

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# Medical Diffusion

This repository contains the code to our paper "Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Synthesis"
(see https://arxiv.org/abs/2211.03364).

![Generated Samples by our Medical Diffusion model](https://raw.githubusercontent.com/FirasGit/medicaldiffusion/master/assets/generated_samples.gif)

# System Requirements
This code has been tested on Ubuntu 20.04 and an NVIDIA Quadro RTX 6000 GPU. Furthermore it was developed using Python v3.8.

# Setup
In order to run our model, we suggest you create a virtual environment 
```
conda create -n medicaldiffusion python=3.8
``` 
and activate it with 
```
conda activate medicaldiffusion
```
Subsequently, download and install the required libraries by running 
```
pip install -r requirements.txt
```

# Training
Once all libraries are installed and the datasets have been downloaded, you are ready to train the model:

First, we need to train the three-dimensional VQ-GAN model. To do so in the BraTS dataset, you can run the following command:

```
PL_TORCH_DISTRIBUTED_BACKEND=gloo python train/train_vqgan.py dataset=brats dataset.root_dir=<INSERT_PATH_TO_BRATS_DATASET> model=vq_gan_3d model.gpus=1 model.default_root_dir_postfix='flair' model.precision=16 model.embedding_dim=8 model.n_hiddens=16 model.downsample=[2,2,2] model.num_workers=32 model.gradient_clip_val=1.0 model.lr=3e-4 model.discriminator_iter_start=10000 model.perceptual_weight=4 model.image_gan_weight=1 model.video_gan_weight=1 model.gan_feat_weight=4 model.batch_size=2 model.n_codes=16384 model.accumulate_grad_batches=1 
```
Note that you need to provide the path to the dataset (e.g. ```dataset.root_dir='/data/BraTS/BraTS 2020'```) to successfully run the command.

To train the diffusion model in the latent space of the previously trained VQ-GAN model, you need to run the following command
```
python train/train_ddpm.py model=ddpm dataset=brats model.results_folder_postfix='flair' model.vqgan_ckpt=<INSERT_PATH_TO_CHECKPOINT> model.diffusion_img_size=32 model.diffusion_depth_size=32 model.diffusion_num_channels=8 model.dim_mults=[1,2,4,8] model.batch_size=10 model.gpus=1
```
Where you again need to specify the path to the VQ-GAN checkpoint from before (e.g. ```model.vqgan_ckpt='/home/<user>/Desktop/medicaldiffusion/checkpoints/vq_gan/BRATS/flair/lightning_logs/version_0/checkpoints/latest_checkpoint.ckpt'```)

# Train on your own dataset
To simpify the dataloading for your own dataset, we provide a default dataset that simply requires the path to the folder with your NifTI images inside, i.e.

root_dir/					# Path to the folder that contains the images
    ├── img1.nii                # The name of the NifTI file is not important
    ├── img2.nii                    
    ├── img3.nii                     
    ├── ...

All you need to do now is just specify the path to this root directory the way we have dealt with it before, i.e.,

```
PL_TORCH_DISTRIBUTED_BACKEND=gloo python train/train_vqgan.py dataset=default dataset.root_dir=<INSERT_PATH_TO_ROOT_DIRECTORY> model=vq_gan_3d model.gpus=1 model.default_root_dir_postfix='own_dataset' model.precision=16 model.embedding_dim=8 model.n_hiddens=16 model.downsample=[2,2,2] model.num_workers=32 model.gradient_clip_val=1.0 model.lr=3e-4 model.discriminator_iter_start=10000 model.perceptual_weight=4 model.image_gan_weight=1 model.video_gan_weight=1 model.gan_feat_weight=4 model.batch_size=2 model.n_codes=16384 model.accumulate_grad_batches=1 
```
Note that you need to provide the path to the dataset (e.g. ```dataset.root_dir='/../../root_dir/'```) to successfully run the command.

To train the diffusion model in the latent space of the previously trained VQ-GAN model, you need to run the following command
```
python train/train_ddpm.py model=ddpm dataset=default model.results_folder_postfix='own_dataset' model.vqgan_ckpt=<INSERT_PATH_TO_CHECKPOINT> model.diffusion_img_size=32 model.diffusion_depth_size=32 model.diffusion_num_channels=8 model.dim_mults=[1,2,4,8] model.batch_size=10 model.gpus=1
```
Where you again need to specify the path to the VQ-GAN checkpoint from before (e.g. ```model.vqgan_ckpt='/home/<user>/Desktop/medicaldiffusion/checkpoints/vq_gan/DEFAULT/own_dataset/lightning_logs/version_0/checkpoints/latest_checkpoint.ckpt'```)

# Citation
To cite our work, please use
```
@misc{https://doi.org/10.48550/arxiv.2211.03364,
  doi = {10.48550/ARXIV.2211.03364},
  url = {https://arxiv.org/abs/2211.03364},
  author = {Khader, Firas and Mueller-Franzes, Gustav and Arasteh, Soroosh Tayebi and Han, Tianyu and Haarburger, Christoph and Schulze-Hagen, Maximilian and Schad, Philipp and Engelhardt, Sandy and Baessler, Bettina and Foersch, Sebastian and Stegmaier, Johannes and Kuhl, Christiane and Nebelung, Sven and Kather, Jakob Nikolas and Truhn, Daniel},
  title = {Medical Diffusion - Denoising Diffusion Probabilistic Models for 3D Medical Image Generation},
  publisher = {arXiv},
  year = {2022},
}
```

# Acknowledgement
This code is heavily build on the following repositories:

(1) https://github.com/SongweiGe/TATS

(2) https://github.com/lucidrains/denoising-diffusion-pytorch

(3) https://github.com/lucidrains/video-diffusion-pytorch

medicaldiffusion

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