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 this https URL

我们提出了金字塔注意力网络（PAN）用于可变形医学图像配准，通过引入双流金字塔编码器和局部多头注意力变换器实现特征表示和分析运动模式，取得了较好的配准性能。