Duan Taisen, Chen Wenkang, Ruan Meilin, Zhang Xuejun, Shen Shaofei, Gu Weiyu
School of Computer, Electronics and Information, Guangxi University, Nanning 530004, People's Republic of China.
Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, People's Republic of China.
Phys Med Biol. 2025 Jan 7;70(2). doi: 10.1088/1361-6560/ad9e69.
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods have achieved revolutionary improvements in processing speed and automation, showing great potential, especially in unsupervised learning. This paper briefly introduces the core concepts of deep learning-based unsupervised image registration, followed by an in-depth discussion of innovative network architectures and a detailed review of these studies, highlighting their unique contributions. Additionally, this paper explores commonly used loss functions, datasets, and evaluation metrics. Finally, we discuss the main challenges faced by various categories and propose potential future research topics. This paper surveys the latest advancements in unsupervised deep neural network-based medical image registration methods, aiming to help active readers interested in this field gain a deep understanding of this exciting area.
近几十年来，医学图像配准技术取得了重大发展，成为医学图像分析的核心技术之一。随着深度学习的兴起，基于深度学习的医学图像配准方法在处理速度和自动化方面取得了革命性的进步，展现出巨大的潜力，尤其是在无监督学习方面。本文简要介绍了基于深度学习的无监督图像配准的核心概念，随后深入讨论了创新的网络架构，并对这些研究进行了详细综述，突出了它们的独特贡献。此外，本文还探讨了常用的损失函数、数据集和评估指标。最后，我们讨论了各类方法面临的主要挑战，并提出了未来可能的研究课题。本文综述了基于无监督深度神经网络的医学图像配准方法的最新进展，旨在帮助对该领域感兴趣的读者深入了解这一令人兴奋的领域。
