The advent of deep-learning-based registration networks has addressed the time-consuming challenge in traditional iterative methods.However, the potential of current registration networks for comprehensively capturing spatial relationships has not been fully explored, leading to inadequate performance in large-deformation image registration.The pure convolutional neural networks (CNNs) neglect feature enhancement, while current Transformer-based networks are susceptible to information redundancy.To alleviate these issues, we propose a pyramid attention network (PAN) for deformable medical image registration.Specifically, the proposed PAN incorporates a dual-stream pyramid encoder with channel-wise attention to boost the feature representation.Moreover, a multi-head local attention Transformer is introduced as decoder to analyze motion patterns and generate deformation fields.Extensive experiments on two public brain magnetic resonance imaging (MRI) datasets and one abdominal MRI dataset demonstrate that our method achieves favorable registration performance, while outperforming several CNN-based and Transformer-based registration networks.Our code is publicly available at https://github.com/JuliusWang-7/PAN.

翻译：基于深度学习的配准网络的出现解决了传统迭代方法耗时长的难题。然而，现有配准网络在全面捕捉空间关系方面的潜力尚未被充分挖掘，导致在大形变图像配准中性能不足。纯卷积神经网络（CNN）忽略了特征增强，而当前基于Transformer的网络容易受到信息冗余的影响。为缓解这些问题，我们提出了一种用于可变形医学图像配准的金字塔注意力网络（PAN）。具体而言，所提出的PAN采用带有通道注意力的双流金字塔编码器来增强特征表示。此外，引入多头局部注意力Transformer作为解码器，以分析运动模式并生成形变场。在两个公开脑部磁共振成像（MRI）数据集和一个腹部MRI数据集上的大量实验表明，我们的方法实现了优异的配准性能，同时优于多个基于CNN和基于Transformer的配准网络。我们的代码已在https://github.com/JuliusWang-7/PAN公开。