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.

翻译：仿射图像配准是医学图像分析的基石。尽管经典算法能实现卓越的准确性，但每对图像都需要解决耗时的优化问题。深度学习方法学习一个将图像对映射到输出变换的函数，模型评估速度虽快，但捕获大尺度变换颇具挑战性，且网络在测试图像特征（如分辨率）偏离训练域时往往表现不佳。大多数仿射方法对解剖结构不敏感，这意味着若算法考虑图像中的所有结构，配准将不准确。我们通过SynthMorph解决这些缺陷——这是一种易用的深度学习工具，可直接对磁共振扫描仪输出的任何脑部图像进行仿射-可变形联合配准，无需预处理。首先，我们采用一种策略，利用标签图合成的多样化图像训练网络，使其在训练中未见的采集参数下仍保持稳健性能。其次，我们优化特定解剖标签的空间重叠率，使网络能够区分感兴趣解剖结构与非相关组织，从而消除需排除干扰内容的预处理步骤。第三，我们将仿射模型与可变形超网络结合，允许用户在配准过程中根据自身数据选择最优形变场正则化程度，且耗时仅为经典方法的几分之一。我们严格分析了不同竞争架构学习仿射变换的方式，并在极度多样化的神经影像数据集中对比当前最先进的配准工具，旨在真实反映方法在实际场景中的表现。SynthMorph展现出稳定且更高的准确性。该工具作为完整的端到端脑部磁共振配准方案，可从https://w3id.org/synthmorph获取。