  Affine image registration is a cornerstone of medical image analysis. While
classical algorithms can achieve excellent accuracy, they solve a
time-consuming optimization for every image pair. Deep-learning (DL) methods
learn a function that maps an image pair to an output transform. Evaluating the
function is fast, but capturing large transforms can be challenging, and
networks tend to struggle if a test-image characteristic shifts from the
training domain, such as resolution. Most affine methods are agnostic to the
anatomy the user wishes to align, meaning the registration will be inaccurate
if algorithms consider all structures in the image. We address these
shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use
DL tool for joint affine-deformable registration of any brain image without
preprocessing. First, we leverage a strategy that trains networks with widely
varying images synthesized from label maps, yielding robust performance for
image types unseen at training. Second, we optimize the spatial overlap of
select anatomical labels. This enables networks to distinguish anatomy of
interest from irrelevant structures, removing the need for preprocessing that
excludes content that may impinge on anatomy-specific registration. Third, we
combine the affine model with a deformable hypernetwork that lets users choose
the optimal deformation-field regularity for their specific data, at
registration time, in a fraction of the time required by classical methods. We
analyze how competing architectures learn affine transforms and compare
state-of-the-art registration tools across an extremely diverse set of
neuroimaging data, aiming to truly capture the behavior of methods in the real
world. SynthMorph demonstrates high accuracy and is available at
https://w3id.org/synthmorph, as a single complete end-to-end solution for
registration of brain MRI.
