Affine image registration is a cornerstone of medical-image processing and
analysis. While classical algorithms can achieve excellent accuracy, they solve
a time-consuming optimization for every new image pair. Deep-learning (DL)
methods learn a function that maps an image pair to an output transform.
Evaluating the functions 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 the contrast or resolution. A majority
of affine methods are also agnostic to the anatomy the user wishes to align;
the registration will be inaccurate if algorithms consider all structures in
the image. We address these shortcomings with a fast, robust, and easy-to-use
DL tool for affine and deformable registration of any brain image without
preprocessing, right off the MRI scanner. First, we rigorously analyze how
competing architectures learn affine transforms across a diverse set of
neuroimaging data, aiming to truly capture the behavior of methods in the real
world. Second, we leverage a recent strategy to train networks with wildly
varying images synthesized from label maps, yielding robust performance across
acquisition specifics. Third, we optimize the spatial overlap of select
anatomical labels, which enables networks to distinguish between anatomy of
interest and irrelevant structures, removing the need for preprocessing that
excludes content that would otherwise reduce the accuracy of anatomy-specific
registration. We combine the affine model with prior work on deformable
registration and test brain-specific registration across a landscape of MRI
protocols unseen at training, demonstrating consistent and improved accuracy
compared to existing tools. We distribute our code and tool at
this https URL, providing a single complete end-to-end solution
for registration of brain MRI.

本文介绍了一种基于深度学习的快速、稳健、易于使用的工具，用于进行任何脑图像的仿射变换和变形配准，无需预处理，即可完成。我们使用神经影像数据集仔细分析了深度学习方法，发现配准的准确性可以得到保证。同时，我们结合了变形配准和空间标签匹配来实现无需预处理的脑部解剖配准方法。