This paper presents an efficient feature-based approach to initialize non-linear image registration. Today, nonlinear image registration is dominated by methods relying on intensity-based similarity measures. A good estimate of the initial transformation is essential, both for traditional iterative algorithms and for recent one-shot deep learning (DL)-based alternatives. The established approach to estimate this starting point is to perform affine registration, but this may be insufficient due to its parsimonious, global, and non-bending nature. We propose an improved initialization method that takes advantage of recent advances in DL-based segmentation techniques able to instantly estimate fine-grained regional delineations with state-of-the-art accuracies. Those segmentations are used to produce local, anatomically grounded, feature-based affine matchings using iteration-free closed-form expressions. Estimated local affine transformations are then fused, with the log-Euclidean polyaffine framework, into an overall dense diffeomorphic transformation. We show that, compared to its affine counterpart, the proposed initialization leads to significantly better alignment for both traditional and DL-based non-linear registration algorithms. The proposed approach is also more robust and significantly faster than commonly used affine registration algorithms such as FSL FLIRT.

翻译：本文提出了一种基于特征的高效方法，用于初始化非线性图像配准。当前，非线性图像配准主要由依赖基于强度的相似性度量的方法主导。无论是对于传统的迭代算法，还是对于近期基于深度学习（DL）的单次推理替代方案，初始变换的良好估计都至关重要。目前估计这一初始点的既定方法是执行仿射配准，但由于其参数简约、全局且无弯曲的特性，这可能并不充分。我们提出了一种改进的初始化方法，该方法利用了基于深度学习的分割技术的最新进展，这些技术能够以最先进的精度即时估计细粒度的区域轮廓。这些分割结果被用于通过免迭代的闭式表达式，生成局部的、基于解剖结构的、基于特征的仿射匹配。估计出的局部仿射变换随后通过对数欧几里得多仿射框架，融合成一个整体的稠密微分同胚变换。我们证明，与仿射初始化相比，所提出的初始化方法能显著提升传统和基于深度学习的非线性配准算法的对齐效果。所提出的方法也比常用的仿射配准算法（如FSL FLIRT）更具鲁棒性，且速度显著更快。