uniGradICON
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The official website for uniGradICON: A Foundation Model for Medical Image Registration
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:
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