Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable 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：过去十年中，深度学习技术极大地推动了医学图像配准领域的发展。初期研究如基于ResNet和U-Net的网络架构，为深度学习驱动的图像配准奠定了基础。后续在基于深度学习的配准方法中，关于相似性度量、形变正则化及不确定性估计等多个方面取得了进展。这些进展不仅丰富了可变形图像配准领域，还促进了其在图谱构建、多图谱分割、运动估计及二维-三维配准等广泛任务中的应用。本文全面概述了基于深度学习的图像配准领域最新进展。我们首先简要介绍深度学习图像配准的核心概念，随后深入探讨创新的网络架构、针对配准任务的损失函数，以及配准不确定性估计方法。此外，本文还探讨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，我们重点阐述了这些新技术在医学成像中的实际应用，并展望了基于深度学习的图像配准的未来发展方向。