  We present HyperMorph, a learning-based strategy for deformable image
registration that removes the need to tune important registration
hyperparameters during training. Classical registration methods solve an
optimization problem to find a set of spatial correspondences between two
images, while learning-based methods leverage a training dataset to learn a
function that generates these correspondences. The quality of the results for
both types of techniques depends greatly on the choice of hyperparameters.
Unfortunately, hyperparameter tuning is time-consuming and typically involves
training many separate models with various hyperparameter values, potentially
leading to suboptimal results. To address this inefficiency, we introduce
amortized hyperparameter learning for image registration, a novel strategy to
learn the effects of hyperparameters on deformation fields. The proposed
framework learns a hypernetwork that takes in an input hyperparameter and
modulates a registration network to produce the optimal deformation field for
that hyperparameter value. In effect, this strategy trains a single, rich model
that enables rapid, fine-grained discovery of hyperparameter values from a
continuous interval at test-time. We demonstrate that this approach can be used
to optimize multiple hyperparameters considerably faster than existing search
strategies, leading to a reduced computational and human burden and increased
flexibility. We also show that this has several important benefits, including
increased robustness to initialization and the ability to rapidly identify
optimal hyperparameter values specific to a registration task, dataset, or even
a single anatomical region - all without retraining the HyperMorph model. Our
code is publicly available at http://voxelmorph.mit.edu.
