Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as ResNet-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, 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深刻重塑了医学图像配准领域。初期基于ResNet和U-Net等网络架构的研究为深度学习在图像配准中的应用奠定了基础。随后，在相似性度量、形变正则化以及不确定性估计等深度学习配准的各个方向取得了持续进展。这些进步不仅丰富了图像配准理论，还推动了其在图谱构建、多图谱分割、运动估计及二维-三维配准等广泛任务中的实际应用。本文全面概述了基于深度学习的图像配准最新进展。我们首先简要介绍深度学习配准的核心概念，继而深入探讨创新性网络架构、面向配准的损失函数以及配准不确定性估计方法。此外，本文还研究了适用于配准任务中深度学习模型性能评估的恰当指标。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。