Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, network architectures, and uncertainty estimation. These advancements have not only enriched the field of image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration.

翻译：深度学习技术在过去十年中从根本上重塑了医学图像配准领域。早期发展，如基于回归和基于U-Net的网络，为深度学习在图像配准中的应用奠定了基础。随后，在基于深度学习的配准诸多方面取得了进展，包括相似性度量、形变正则化、网络架构和不确定性估计。这些进步不仅丰富了图像配准领域，也促进了其在广泛任务中的应用，如图集构建、多图谱分割、运动估计和2D-3D配准。本文全面概述了基于深度学习的图像配准的最新进展。我们首先简要介绍基于深度学习的图像配准的核心概念。随后，详细探讨创新的网络架构、配准专用的损失函数以及配准不确定性估计方法。此外，本文还研究了评估深度学习模型在配准任务中性能的合适评价指标。最后，我们强调了这些新技术在医学成像中的实际应用，并讨论了基于深度学习的图像配准的未来前景。