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 https://w3id.org/synthmorph, providing a single complete end-to-end solution for registration of brain MRI.

翻译：摘要：仿射图像配准是医学图像处理与分析的基石。传统算法虽能实现卓越精度，但每对新图像都需耗时求解优化问题。深度学习（DL）方法通过学习将图像对映射为输出变换的函数，评估函数速度快，但捕捉大尺度变换仍具挑战性，且当测试图像特征（如对比度或分辨率）偏离训练域时，网络常难以适应。多数仿射方法也忽视用户期望对齐的解剖结构——若算法考虑图像中所有结构，配准精度将下降。我们提出一种快速、鲁棒且易于使用的DL工具，无需预处理即可直接对MRI扫描仪输出的任意脑图像进行仿射与可变形配准，以解决上述不足。首先，我们严谨分析不同竞争架构如何跨多样神经影像数据学习仿射变换，旨在真实捕捉方法在实际场景中的表现。其次，我们利用近期提出的策略，通过标签图合成的形态各异图像训练网络，从而获得跨采集特异性的鲁棒性能。第三，我们优化选定解剖标签的空间重叠率，使网络能区分目标解剖与无关结构，消除需剔除会降低解剖特异性配准精度内容的预处理需求。我们将仿射模型与可变形配准先前工作结合，在训练中未见过的多样MRI协议下测试脑特异性配准，相比现有工具展现出一致且更优的精度。我们于https://w3id.org/synthmorph发布代码与工具，为脑MRI配准提供单一完整的端到端解决方案。