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.

翻译：图像配准是医学图像分析的基本需求。基于深度学习的深度配准方法因其快速端到端配准能力而得到广泛认可。许多深度配准方法通过级联网络迭代执行多步粗到细配准，取得了领先性能。近年来，非迭代式粗细粒度（NICE）配准方法被提出，可在单个网络中实现粗到细配准，在配准精度和运行时间上均展现出优势。然而，现有NICE配准方法主要聚焦于可变形配准，而作为常见前提的仿射配准仍依赖于耗时的传统优化方法或额外仿射配准网络。此外，现有NICE配准方法受限于卷积运算的固有局部性。Transformer可凭借其捕捉长距离依赖的能力解决该局限，但Transformer在NICE配准中的优势尚未被探索。本研究提出了一种非迭代式粗细粒度Transformer网络（NICE-Trans）用于图像配准。我们的NICE-Trans是首个（i）在单个网络中实现联合仿射与可变形粗到细配准，以及（ii）将Transformer嵌入NICE配准框架以建模图像间长距离相关性的深度配准方法。在七个公开数据集上的大量实验表明，我们的NICE-Trans在配准精度和运行时间上均优于当前最先进的配准方法。