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<img src="ICON_universal_model.png" width="35" height="35"> 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. To get you started quickly there is also an easy to use Slicer extension.

teaser

uniGradICON: A Foundation Model for Medical Image Registration
Tian, Lin and Greer, Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Estepar, Raul San Jose and Bouix, Sylvain and Rushmore, Richard and Niethammer, Marc
MICCAI 2024 https://arxiv.org/abs/2403.05780

multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Demir, Basar and Tian, Lin and Greer, Thomas Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Estepar, Raul San Jose and Bouix, Sylvain and Rushmore, Richard Jarrett and Ebrahim, Ebrahim and Niethammer, Marc
MICCAI Workshop on Biomedical Image Registration (WBIR) 2024 https://arxiv.org/abs/2408.00221

Please (currently) cite as:

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

@article{demir2024multigradicon,
  title={multiGradICON: A Foundation Model for Multimodal Medical Image Registration},
  author={Demir, Basar and Tian, Lin and Greer, Thomas Hastings and Kwitt, Roland and Vialard, Francois-Xavier and Estepar, Raul San Jose and Bouix, Sylvain and Rushmore, Richard Jarrett and Ebrahim, Ebrahim and Niethammer, Marc},
  journal={arXiv preprint arXiv:2408.00221},
  year={2024}
}

Training and testing data

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

Training data:

Testing data:

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

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

To register without instance optimization (IO)

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 --io_iterations None

To use a different similarity measure in the IO. We currently support three similarity measures

LNCC: lncc
Squared LNCC: lncc2
MIND SSC: mind

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 --io_iterations 50 --io_sim lncc2

To load specific model weight in the inference. We currently support uniGradICON and multiGradICON.

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 --model multigradicon

To warp an image

unigradicon-warp --fixed [fixed_image_file_name] --moving [moving_image_file_name]  --transform trans.hdf5 --warped_moving_out warped.nii.gz --linear

To warp a label map

unigradicon-warp --fixed [fixed_image_file_name] --moving [moving_image_segmentation_file_name]  --transform trans.hdf5 --warped_moving_out warped_seg.nii.gz --nearest_neighbor

We also provide a colab demo.

Slicer Extension

A Slicer extensions is available here . It is an official Slicer Extension and can be installed via the Slicer Extension Manager. This requires Slicer >=5.7.0. Please make sure to install the Slicer PyTorch extension before as uniGradICON depends on it.

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: result