Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
arxiv(2023)
摘要
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
anatomy, meaning the registration will be inaccurate if algorithms consider all
structures in the image.
We address these shortcomings with SynthMorph, an easy-to-use DL tool for
joint affine-deformable registration of any brain image without preprocessing,
right off the MRI scanner. First, we leverage a strategy to train networks with
wildly varying images synthesized from label maps, yielding robust performance
across acquisition specifics 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 which would 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 rigorously 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 consistent and improved accuracy. It is
available at https://w3id.org/synthmorph, as a single complete end-to-end
solution for registration of brain MRI.
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关键词
registration,anatomy-aware,acquisition-agnostic
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