Deformable image registration is a fundamental step for medical image
analysis. Recently, transformers have been used for registration and
outperformed Convolutional Neural Networks (CNNs). Transformers can capture
long-range dependence among image features, which have been shown beneficial
for registration. However, due to the high computation/memory loads of
self-attention, transformers are typically used at downsampled feature
resolutions and cannot capture fine-grained long-range dependence at the full
image resolution. This limits deformable registration as it necessitates
precise dense correspondence between each image pixel. Multi-layer Perceptrons
(MLPs) without self-attention are efficient in computation/memory usage,
enabling the feasibility of capturing fine-grained long-range dependence at
full resolution. Nevertheless, MLPs have not been extensively explored for
image registration and are lacking the consideration of inductive bias crucial
for medical registration tasks. In this study, we propose the first
correlation-aware MLP-based registration network (CorrMLP) for deformable
medical image registration. Our CorrMLP introduces a correlation-aware
multi-window MLP block in a novel coarse-to-fine registration architecture,
which captures fine-grained multi-range dependence to perform correlation-aware
coarse-to-fine registration. Extensive experiments with seven public medical
datasets show that our CorrMLP outperforms state-of-the-art deformable
registration methods.

本研究提出了基于多窗口感知的 MLP 网络（CorrMLP）用于可变形医学图像配准，通过新颖的粗到细的配准架构捕捉细粒度的多范围依赖关系，进行多范围感知的粗到细配准，实验证明 CorrMLP 优于最先进的可变形配准方法。