Image registration is a fundamental requirement for medical image analysis.
Deep registration methods based on deep learning have been widely recognized
for their capabilities to perform fast end-to-end registration. Many deep
registration methods achieved state-of-the-art performance by performing
coarse-to-fine registration, where multiple registration steps were iterated
with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE)
registration methods have been proposed to perform coarse-to-fine registration
in a single network and showed advantages in both registration accuracy and
runtime. However, existing NICE registration methods mainly focus on deformable
registration, while affine registration, a common prerequisite, is still
reliant on time-consuming traditional optimization-based methods or extra
affine registration networks. In addition, existing NICE registration methods
are limited by the intrinsic locality of convolution operations. Transformers
may address this limitation for their capabilities to capture long-range
dependency, but the benefits of using transformers for NICE registration have
not been explored. In this study, we propose a Non-Iterative Coarse-to-finE
Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the
first deep registration method that (i) performs joint affine and deformable
coarse-to-fine registration within a single network, and (ii) embeds
transformers into a NICE registration framework to model long-range relevance
between images. Extensive experiments with seven public datasets show that our
NICE-Trans outperforms state-of-the-art registration methods on both
registration accuracy and runtime.

本研究提出了一种非迭代粗到精变换网络 (NICE-Trans) 用于图像配准，它是第一种在单个网络中执行联合仿射和可变形的粗到精变换的深度配准方法，并将 Transformer 嵌入到 NICE 配准框架中以建模图像之间的远程相关性。通过与七个公共数据集的广泛实验表明，我们的 NICE-Trans 在配准精度和运行时间上优于现有的配准方法。