Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research.

翻译：医学图像配准通过融合不同时间、角度或模态下采集的图像信息，对疾病诊断与治疗至关重要。尽管已有若干综述回顾了医学图像配准的发展历程，但这些综述尚未系统性地总结现有医学图像配准方法的方法论。为此，我们从传统方法与基于深度学习的方向出发，对这些方法进行全面综述，旨在帮助读者快速理解医学图像配准的发展脉络。特别地，我们在每章节末尾重点介绍了视网膜图像配准的最新进展——这一方向尚未引起足够关注。此外，我们还探讨了当前视网膜图像配准面临的挑战，并对未来研究方向提出了见解与展望。