Registration plays an important role in medical image analysis. Deep
learning-based methods have been studied for medical image registration, which
leverage convolutional neural networks (CNNs) for efficiently regressing a
dense deformation field from a pair of images. However, CNNs are limited in its
ability to extract semantically meaningful intra- and inter-image spatial
correspondences, which are of importance for accurate image registration. This
study proposes a novel end-to-end deep learning-based framework for
unsupervised affine and diffeomorphic deformable registration, referred as
ACSGRegNet, which integrates a cross-attention module for establishing
inter-image feature correspondences and a self-attention module for intra-image
anatomical structures aware. Both attention modules are built on transformer
encoders. The output from each attention module is respectively fed to a
decoder to generate a velocity field. We further introduce a gated fusion
module to fuse both velocity fields. The fused velocity field is then
integrated to a dense deformation field. Extensive experiments are conducted on
lumbar spine CT images. Once the model is trained, pairs of unseen lumbar
vertebrae can be registered in one shot. Evaluated on 450 pairs of vertebral CT
data, our method achieved an average Dice of 0.963 and an average distance
error of 0.321mm, which are better than the state-of-the-art (SOTA).

该研究提出了一种新的基于深度学习的框架 ——ACSGRegNet，该框架通过集成交叉关注模块和自关注模块来实现无监督的仿射和差分变形配准，通过在脊柱 CT 图像上进行广泛的实验，已经取得了比现有方法更好的结果。

ACSGRegNet：基于深度学习的腰椎 CT 联合仿射和变形配准的无监督框架，通过交叉自注意融合实现

ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion