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MedSegDiff: Medical Image Segmentation with Diffusion Model

MedSegDiff a Diffusion Probabilistic Model (DPM) based framework for Medical Image Segmentation. The algorithm is elaborated on our paper MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model and MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer.

Diffusion Models work by destroying training data through the successive addition of Gaussian noise, and then learning to recover the data by reversing this noising process. After training, we can use the Diffusion Model to generate data by simply passing randomly sampled noise through the learned denoising process.In this project, we extend this idea to medical image segmentation. We utilize the original image as a condition and generate multiple segmentation maps from random noises, then perform ensembling on them to obtain the final result. This approach captures the uncertainty in medical images and outperforms previous methods on several benchmarks.

A Quick Overview
MedSegDiff-V1 MedSegDiff-V2

News

  • [TOP] Join in our Discord to ask questions and discuss with others.
  • 22-11-30. This project is still quickly updating. Check TODO list to see what will be released next.
  • 22-12-03. BraTs2020 bugs fixed. Example case added.
  • 22-12-15. Fix multi-gpu distributed training.
  • 22-12-16. DPM-Solver ✖️ MedSegDiff DONE 🥳 Now DPM-Solver is avaliable in MedsegDiff. Enjoy its lightning-fast sampling (1000 steps ❌ 20 steps ⭕️) by setting --dpm_solver True.
  • 22-12-23. Fixed some bugs of DPM-Solver.
  • 23-01-31. MedSegDiff-V2 will be avaliable soon 🥳 . Check our paper MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer first.
  • 23-02-07. Optimize workflow in BRATS sampling. Add dataloader for processing raw 3D BRATS data.
  • 23-02-11. Fix bugs 3D BRATS data training bugs, issue 31.
  • 23-03-04. Paper MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model has been officially accepted by MIDL 2023 🥳
  • 23-04-11. A new version based on the v2 framework has been released 🥳. It's more accurate, stable, and domain-adaptable than the previous version, while still not hogging too much of your resources. We've also fixed up a bunch of small things, like the requirement.txt and isic csv files. Huge thanks to all of you who reported issues, you really helped us a lot 🤗. btw, it will run the new version by default. Add "--version 1" if you want run the previous version.
  • 23-04-12. Added a simple evaluation file for isic dataset (script/segmentation_env). Usage: python scripts/segmentation_env.py --inp_pth *folder you save prediction images* --out_pth *folder you save ground truth images*
  • 23-12-05. Paper MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer has been officially accepted by AAAI 2024 🥳
  • 24-10-29. MedSegDiff-V2 has been selected as AAAI-24 Most Influential Paper 🥳

Requirement

pip install -r requirement.txt

Example Cases

Melanoma Segmentation from Skin Images

  1. Download ISIC dataset from https://challenge.isic-archive.com/data/. Your dataset folder under "data" should be like:
data
|   ----ISIC
|       ----Test
|       |   |   ISBI2016_ISIC_Part1_Test_GroundTruth.csv
|       |   |   
|       |   ----ISBI2016_ISIC_Part1_Test_Data
|       |   |       ISIC_0000003.jpg
|       |   |       .....
|       |   |
|       |   ----ISBI2016_ISIC_Part1_Test_GroundTruth
|       |           ISIC_0000003_Segmentation.png
|       |   |       .....
|       |           
|       ----Train
|           |   ISBI2016_ISIC_Part1_Training_GroundTruth.csv
|           |   
|           ----ISBI2016_ISIC_Part1_Training_Data
|           |       ISIC_0000000.jpg
|           |       .....
|           |       
|           ----ISBI2016_ISIC_Part1_Training_GroundTruth
|           |       ISIC_0000000_Segmentation.png
|           |       .....

  1. For training, run: python scripts/segmentation_train.py --data_name ISIC --data_dir *input data direction* --out_dir *output data direction* --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_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --lr 1e-4 --batch_size 8

  2. For sampling, run: python scripts/segmentation_sample.py --data_name ISIC --data_dir *input data direction* --out_dir *output data direction* --model_path *saved model* --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_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --num_ensemble 5

  3. For evaluation, run python scripts/segmentation_env.py --inp_pth *folder you save prediction images* --out_pth *folder you save ground truth images*

In default, the samples will be saved at ./results/

Brain Tumor Segmentation from MRI

  1. Download BRATS2020 dataset from https://www.med.upenn.edu/cbica/brats2020/data.html. Your dataset folder should be like:
data
└───training
│   └───slice0001
│       │   brats_train_001_t1_123_w.nii.gz
│       │   brats_train_001_t2_123_w.nii.gz
│       │   brats_train_001_flair_123_w.nii.gz
│       │   brats_train_001_t1ce_123_w.nii.gz
│       │   brats_train_001_seg_123_w.nii.gz
│   └───slice0002
│       │  ...
└───testing
│   └───slice1000
│       │  ...
│   └───slice1001
│       │  ...

  1. For training, run: python scripts/segmentation_train.py --data_dir (where you put data folder)/data/training --out_dir output data direction --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_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --lr 1e-4 --batch_size 8

  2. For sampling, run: python scripts/segmentation_sample.py --data_dir (where you put data folder)/data/testing --out_dir output data direction --model_path saved model --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_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --num_ensemble 5

Other Examples

...

Run on your own dataset

It is simple to run MedSegDiff on the other datasets. Just write another data loader file following ./guided_diffusion/isicloader.py or ./guided_diffusion/bratsloader.py. Welcome to open issues if you meet any problem. It would be appreciated if you could contribute your dataset extensions. Unlike natural images, medical images vary a lot depending on different tasks. Expanding the generalization of a method requires everyone's efforts.

Suggestions for Hyperparameters and Training

To train a fine model, i.e., MedSegDiff-B in the paper, set the model hyperparameters as:

--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 hyperparameters as:

--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False

To speed up the sampling:

--diffusion_steps 50 --dpm_solver True

run on multiple GPUs:

--multi-gpu 0,1,2 (for example)

training hyperparameters as:

--lr 5e-5 --batch_size 8

and set --num_ensemble 5 in sampling.

Run about 100,000 steps in training will be converged on most of the datasets. Note that although loss will not decrease in most of the later steps, the quality of the results are still improving. Such a process is also observed on the other DPM applications, like image generation. Hope someone smart can tell me why🥲.

I will soon publish its performance under smaller batch size (suitable to run on 24GB GPU) for the need of comparison🤗.

A setting to unleash all its potential is (MedSegDiff++):

--image_size 256 --num_channels 512 --class_cond False --num_res_blocks 12 --num_heads 8 --learn_sigma True --use_scale_shift_norm True --attention_resolutions 24

Then train it with batch size --batch_size 64 and sample it with ensemble number --num_ensemble 25.

Be a part of MedSegDiff ! Authors are YOU !

Welcome to contribute to MedSegDiff. Any technique can improve the performance or speed up the algorithm is appreciated🙏. I am writting MedSegDiff V2, aiming at Nature journals/CVPR like publication. I'm glad to list the contributors as my co-authors🤗.

TODO LIST

  • Fix bugs in BRATS. Add BRATS example.
  • Release REFUGE and DDIT dataloaders and examples
  • Speed up sampling by DPM-solver
  • Inference of depth
  • Fix bugs in Multi-GPU parallel
  • Sample and Vis in training
  • Release pre processing and post processing
  • Release evaluation
  • Deploy on HuggingFace
  • configuration

Thanks

Code copied a lot from openai/improved-diffusion, WuJunde/ MrPrism, WuJunde/ DiagnosisFirst, LuChengTHU/dpm-solver, JuliaWolleb/Diffusion-based-Segmentation, hojonathanho/diffusion, guided-diffusion, bigmb/Unet-Segmentation-Pytorch-Nest-of-Unets, nnUnet, lucidrains/vit-pytorch

Cite

Please cite

@inproceedings{wu2023medsegdiff,
  title={MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model},
  author={Wu, Junde and FU, RAO and Fang, Huihui and Zhang, Yu and Yang, Yehui and Xiong, Haoyi and Liu, Huiying and Xu, Yanwu},
  booktitle={Medical Imaging with Deep Learning},
  year={2023}
}

@article{wu2023medsegdiff,
  title={MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer},
  author={Wu, Junde and Ji, Wei and Fu, Huazhu and Xu, Min and Jin, Yueming and Xu, Yanwu}
  journal={arXiv preprint arXiv:2301.11798},
  year={2023}
}

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