We introduce HyperMorph, a framework that facilitates efficient
hyperparameter tuning in learning-based deformable image registration.
Classical registration algorithms perform an iterative pair-wise optimization
to compute a deformation field that aligns two images. Recent learning-based
approaches leverage large image datasets to learn a function that rapidly
estimates a deformation for a given image pair. In both strategies, the
accuracy of the resulting spatial correspondences is strongly influenced by the
choice of certain hyperparameter values. However, an effective hyperparameter
search consumes substantial time and human effort as it often involves training
multiple models for different fixed hyperparameter values and may lead to
suboptimal registration. We propose an amortized hyperparameter learning
strategy to alleviate this burden by learning the impact of hyperparameters on
deformation fields. We design a meta network, or hypernetwork, that predicts
the parameters of a registration network for input hyperparameters, thereby
comprising a single model that generates the optimal deformation field
corresponding to given hyperparameter values. This strategy enables fast,
high-resolution hyperparameter search at test-time, reducing the inefficiency
of traditional approaches while increasing flexibility. We also demonstrate
additional benefits of HyperMorph, including enhanced robustness to model
initialization and the ability to rapidly identify optimal hyperparameter
values specific to a dataset, image contrast, task, or even anatomical region,
all without the need to retrain models. We make our code publicly available at
http://hypermorph.voxelmorph.net.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) at https://www.melba-journal.or
