Deformable image registration aims to find a dense non-linear spatial
correspondence between a pair of images, which is a crucial step for many
medical tasks such as tumor growth monitoring and population analysis.
Recently, Deep Neural Networks (DNNs) have been widely recognized for their
ability to perform fast end-to-end registration. However, DNN-based
registration needs to explore the spatial information of each image and fuse
this information to characterize spatial correspondence. This raises an
essential question: what is the optimal fusion strategy to characterize spatial
correspondence? Existing fusion strategies (e.g., early fusion, late fusion)
were empirically designed to fuse information by manually defined prior
knowledge, which inevitably constrains the registration performance within the
limits of empirical designs. In this study, we depart from existing
empirically-designed fusion strategies and develop a data-driven fusion
strategy for deformable image registration. To achieve this, we propose an
Automatic Fusion network (AutoFuse) that provides flexibility to fuse
information at many potential locations within the network. A Fusion Gate (FG)
module is also proposed to control how to fuse information at each potential
network location based on training data. Our AutoFuse can automatically
optimize its fusion strategy during training and can be generalizable to both
unsupervised registration (without any labels) and semi-supervised registration
(with weak labels provided for partial training data). Extensive experiments on
two well-benchmarked medical registration tasks (inter- and intra-patient
registration) with eight public datasets show that our AutoFuse outperforms
state-of-the-art unsupervised and semi-supervised registration methods.

提出了一种数据驱动的融合策略，用于可变形图像配准，通过自动优化融合策略，并结合自动融合网络和融合门模块，实现了更好的配准性能，超过了现有的无监督和半监督配准方法。