Affine registration is indispensable in a comprehensive medical image
registration pipeline. However, only a few studies focus on fast and robust
affine registration algorithms. Most of these studies utilize convolutional
neural networks (CNNs) to learn joint affine and non-parametric registration,
while the standalone performance of the affine subnetwork is less explored.
Moreover, existing CNN-based affine registration approaches focus either on the
local misalignment or the global orientation and position of the input to
predict the affine transformation matrix, which are sensitive to spatial
initialization and exhibit limited generalizability apart from the training
dataset. In this paper, we present a fast and robust learning-based algorithm,
Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image
registration. Our method naturally leverages the global connectivity and
locality of the convolutional vision transformer and the multi-resolution
strategy to learn the global affine registration. We evaluate our method on 3D
brain atlas registration and template-matching normalization. Comprehensive
results demonstrate that our method is superior to the existing CNNs-based
affine registration methods in terms of registration accuracy, robustness and
generalizability while preserving the runtime advantage of the learning-based
methods. The source code is available at this https URL

本文提出一种快速且鲁棒的学习算法 C2FViT 用于 3D 医学图像配准，其应用了视觉 Transformer、多分辨率策略和联合仿射非参数配准进行学习，相对于现有的基于 CNN 的仿射配准方法在准确度、鲁棒性和泛化能力上均具优越性，同时保留了基于学习的方法的运行速度优势。