Abstract
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.
Abstract (translated)
光学图像配准是医学图像处理和分析的基础。虽然经典算法可以取得出色的精度,但它们解决每个新图像对的耗时优化问题。深度学习(DL)方法学习一个映射图像对到输出变换的函数。评估函数很快,但捕捉大型变换可能会挑战,如果测试图像的特征从训练领域(如对比度或分辨率)移动,网络往往会挣扎。大部分Affine方法也对用户希望对齐的解剖学结构无偏见;如果算法考虑所有图像结构,配准将不准确。我们使用快速、稳健且易于使用的DL工具,对任何脑图像的Affine和可塑配准进行预处理后从MRI扫描机移除,提供从未在训练中出现过的MRI协议 landscapes 的全面端到端解决方案,比现有工具表现出一致性和精度的提高。我们结合Affine模型与可塑配准领域的先前工作,测试在训练领域从未见过的MRI协议 landscapes 中的脑特定配准,表现出与现有工具一致的精度提高。我们将我们的代码和工具分发到这个https URL,提供脑MRI配准的全面端到端解决方案。
URL
https://arxiv.org/abs/2301.11329
