uniGradICON: A Foundation Model for Medical Image Registration
Lin Tian Hastings Greer Roland Kwitt Francois-Xavier Vialard Raul San Jose Estepar Sylvain Bouix Richard Rushmore Marc Niethammer
Conventional medical image registration approaches directly optimize over the
parameters of a transformation model. These approaches have been highly
successful and are used generically for registrations of different anatomical
regions. Recent deep registration networks are incredibly fast and accurate but
are only trained for specific tasks. Hence, they are no longer generic
registration approaches. We therefore propose uniGradICON, a first step toward
a foundation model for registration providing 1) great performance
\emph{across} multiple datasets which is not feasible for current
learning-based registration methods, 2) zero-shot capabilities for new
registration tasks suitable for different acquisitions, anatomical regions, and
modalities compared to the training dataset, and 3) a strong initialization for
finetuning on out-of-distribution registration tasks. UniGradICON unifies the
speed and accuracy benefits of learning-based registration algorithms with the
generic applicability of conventional non-deep-learning approaches. We
extensively trained and evaluated uniGradICON on twelve different public
datasets. Our code and the uniGradICON model are available at
https://github.com/uncbiag/uniGradICON.