uniGradICON: A Foundation Model for Medical Image Registration

This the official repository for uniGradICON: A Foundation Model for Medical Image Registration

uniGradICON is based on GradICON but trained on several different datasets (see details below). The result is a deep-learning-based registration model that works well across datasets. More results can be found here.

Please (currently) cite as:

@misc{tian2024unigradicon,
      title={uniGradICON: A Foundation Model for Medical Image Registration}, 
      author={Lin Tian and Hastings Greer and Roland Kwitt and Francois-Xavier Vialard and Raul San Jose Estepar and Sylvain Bouix and Richard Rushmore and Marc Niethammer},
      year={2024},
      eprint={2403.05780},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Training and testing data

uniGradICON has currently been trained and tested on the following datasets.

Training data:

	Dataset	Anatomical region	# of patients	# per patient	# of pairs	Type	Modality
1.	COPDGene	Lung	899	2	899	Intra-pat.	CT
2.	OAI	Knee	2532	1	3,205,512	Inter-pat.	MRI
3.	HCP	Brain	1076	1	578,888	Inter-pat.	MRI
4.	L2R-Abdomen	Abdomen	30	1	450	Inter-pat.	CT

Testing data:

	Dataset	Anatomical region	# of patients	# per patient	# of pairs	Type	Modality
5.	Dirlab-COPDGene	Lung	10	2	10	Intra-pat.	CT
6.	OAI-test	Knee	301	1	301	Inter-pat.	MRI
7.	HCP-test	Brain	32	1	100	Inter-pat.	MRI
8.	L2R-NLST-val	Lung	10	2	10	Intra-pat.	CT
9.	L2R-OASIS-val	Brain	20	1	19	Inter-pat.	MRI
10.	IXI-test	Brain	115	1	115	Atlas-pat.	MRI
11.	L2R-CBCT-val	Lung	3	3	6	Intra-pat.	CT/CBCT
12.	L2R-CTMR-val	Abdomen	3	2	3	Intra-pat.	CT/MRI
13.	L2R-CBCT-train	Lung	3	11	22	Intra-pat.	CT/CBCT

Get involved

Our goal is to continuously improve the uniGradICON model, e.g., by training on more datasets with additional diversity. Feel free to point us to datasets that should be included or let us know if you want to help with future developments.

Easy to use and install

To use:

python3 -m venv unigradicon_virtualenv
source unigradicon_virtualenv/bin/activate

pip install unigradicon

wget https://www.hgreer.com/assets/slicer_mirror/RegLib_C01_1.nrrd
wget https://www.hgreer.com/assets/slicer_mirror/RegLib_C01_2.nrrd

unigradicon-register --fixed=RegLib_C01_2.nrrd --fixed_modality=mri --moving=RegLib_C01_1.nrrd --moving_modality=mri --transform_out=trans.hdf5 --warped_moving_out=warped_C01_1.nrrd

We also provide a colab demo.

Plays well with others

UniGradICON is set up to work with Itk images and transforms. So you can easily read and write images and display resulting transformations for example in 3D Slicer.

The result can be viewed in 3D Slicer: