医学图像配准及其在视网膜图像中的应用：综述
Medical Image Registration and Its Application in Retinal Images: A Review
AI总结：
本文从传统方法和深度学习方法全面综述了医学图像配准，并特别回顾了视网膜图像配准的进展、挑战与未来展望。
摘要：
医学图像配准对于疾病诊断和治疗至关重要，它能够融合在不同时间、角度或模态下捕获的图像的多样化信息。尽管已有若干综述回顾了医学图像配准的发展，但这些综述并未系统地总结现有医学图像配准方法的方法论。为此，我们从传统方法和基于深度学习的方法两个方向对这些方法进行了全面回顾，旨在帮助读者快速了解医学图像配准的发展。特别地，我们在每节末尾回顾了视网膜图像配准的最新进展，这一领域此前并未引起太多关注。此外，我们还讨论了视网膜图像配准当前面临的挑战，并为未来研究提供了见解与展望。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
