multiGradICON: A Foundation Model for Multimodal Medical Image Registration
<|reference_start|>multiGradICON: A Foundation Model for Multimodal Medical Image Registration: Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventional non-deep-learning-based approaches, anatomy-specific. Recently, a universal deep registration approach, uniGradICON, has been proposed. However, uniGradICON focuses on monomodal image registration. In this work, we therefore develop multiGradICON as a first step towards universal *multimodal* medical image registration. Specifically, we show that 1) we can train a DL registration model that is suitable for monomodal *and* multimodal registration; 2) loss function randomization can increase multimodal registration accuracy; and 3) training a model with multimodal data helps multimodal generalization. Our code and the multiGradICON model are available at https://github.com/uncbiag/uniGradICON.<|reference_end|>
@article{demir2024multigradicon:,
    title={multiGradICON: A Foundation Model for Multimodal Medical Image
  Registration},
    author={Basar Demir, Lin Tian, Thomas Hastings Greer, Roland Kwitt,
  Francois-Xavier Vialard, Raul San Jose Estepar, Sylvain Bouix, Richard
  Jarrett Rushmore, Ebrahim Ebrahim, Marc Niethammer},
    journal={arXiv preprint arXiv:2408.00221},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00221},
    primaryClass={eess.IV cs.CV}
}