We provide the official Pytorch implementation of the paper Diffusion Models for Implicit Image Segmentation Ensembles by Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, and Philippe C. Cattin.

The implementation of Denoising Diffusion Probabilistic Models presented in the paper is based on openai/improved-diffusion.

Diffusion models have shown impressive performance for generative modelling of images. In this paper, we present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images. To generate an image specific segmentation, we train the model on the ground truth segmentation, and use the image as a prior during training and in every step during the sampling process. With the given stochastic sampling process, we can generate a distribution of segmentation masks. This property allows us to compute pixel-wise uncertainty maps of the segmentation, and allows an implicit ensemble of segmentations that increases the segmentation performance. We evaluate our method on the BRATS2020 dataset for brain tumor segmentation. Compared to state-of-the-art segmentation models, our approach yields good segmentation results and, additionally, detailed uncertainty maps.

We evaluated our method on the BRATS2020 dataset. For our dataloader, which can be found in the file guided_diffusion/bratsloader.py, the 2D slices need to be stored in the following structure:

data
└───training
│   └───slice0001
│       │   t1.nii.gz
│       │   t2.nii.gz
│       │   flair.nii.gz
│       │   t1ce.nii.gz
│       │   seg.nii.gz
│   └───slice0002
│       │  ...
└───testing
│   └───slice1000
│       │   t1.nii.gz
│       │   t2.nii.gz
│       │   flair.nii.gz
│       │   t1ce.nii.gz
│   └───slice1001
│       │  ...

A mini-example can be found in the folder data. If you want to apply our code to another dataset, make sure the loaded image has attached the ground truth segmentation as the last channel.

We set the flags as follows:

MODEL_FLAGS="--image_size 256 --num_channels 128 --class_cond False --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 1e-4 --batch_size 10"

To train the segmentation model, run

python3 scripts/segmentation_train.py --data_dir ./data/training $TRAIN_FLAGS $MODEL_FLAGS $DIFFUSION_FLAGS

The model will be saved in the results folder. For sampling an ensemble of 5 segmentation masks with the DDPM approach, run:

python scripts/segmentation_sample.py  --data_dir ./data/testing  --model_path ./results/savedmodel.pt --num_ensemble=5 $MODEL_FLAGS $DIFFUSION_FLAGS

The generated segmentation masks will be stored in the results folder. A visualization of the sampling process is done using Visdom.

If you use this code, please cite

@misc{wolleb2021diffusion,
      title={Diffusion Models for Implicit Image Segmentation Ensembles}, 
      author={Julia Wolleb and Robin Sandkühler and Florentin Bieder and Philippe Valmaggia and Philippe C. Cattin},
      year={2021},
      eprint={2112.03145},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}