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.

翻译：可变形图像配准旨在寻找一对图像之间的密集非线性空间对应关系，这对于肿瘤生长监测和人群分析等许多医学任务至关重要。近年来，深度神经网络因其能够执行快速端到端配准而得到广泛认可。然而，基于深度神经网络的配准需要探索每幅图像的空间信息并融合这些信息以表征空间对应关系。这引出了一个关键问题：什么是最优的融合策略来表征空间对应关系？现有的融合策略（例如，早期融合、晚期融合）是通过手动定义的先验知识经验性地设计来融合信息，这不可避免地限制了配准性能于经验设计的范畴内。在本研究中，我们脱离现有的经验性设计融合策略，为可变形图像配准开发了一种数据驱动的融合策略。为此，我们提出了一个自动融合网络，该网络提供了在网络内许多潜在位置融合信息的灵活性。我们还提出了一个融合门控模块，用于根据训练数据控制在每个潜在网络位置如何融合信息。我们的自动融合网络可以在训练过程中自动优化其融合策略，并可推广到无监督配准（无任何标签）和半监督配准（提供部分训练数据的弱标签）。在八个公开数据集上针对两个基准医学配准任务（患者间和患者内配准）进行的大量实验表明，我们的自动融合网络优于最先进的无监督和半监督配准方法。