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.

中文翻译：

---

医学影像配准中的深度学习调查：新技术、不确定性、评估指标等


在过去十年中，深度学习技术极大地改变了医学影像配准领域。最初的发展，例如基于回归和基于 U-Net 的网络，为图像配准中的深度学习奠定了基础。随后在基于深度学习的配准的各个方面都取得了进展，包括相似性度量、变形正则化、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，还促进了其在广泛任务中的应用，包括图集构建、多图集分割、运动估计和 2D-3D 配准。在本文中，我们全面概述了基于深度学习的图像配准的最新进展。我们首先简要介绍了基于深度学习的图像配准的核心概念。然后，我们深入研究了创新的网络架构、特定于注册的损失函数以及估计注册不确定性的方法。此外，本文还探讨了用于评估深度学习模型在配准任务中的性能的适当评估指标。最后，我们重点介绍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。