Nie Qiushi, Zhang Xiaoqing, Hu Yan, Gong Mingdao, Liu Jiang
Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Vis Comput Ind Biomed Art. 2024 Aug 21;7(1):21. doi: 10.1186/s42492-024-00173-8.
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, they have not systematically summarized the existing medical image registration methods. To this end, a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives, aiming to help audiences quickly understand the development of medical image registration. In particular, we review recent advances in retinal image registration, which has not attracted much attention. In addition, current challenges in retinal image registration are discussed and insights and prospects for future research provided.
医学图像配准对于疾病诊断和治疗至关重要，因为它能够融合在不同时间、角度或模态下获取的图像的各种信息。尽管已有多项综述回顾了医学图像配准的发展，但它们并未系统地总结现有的医学图像配准方法。为此，本文从传统方法和基于深度学习的方法两个角度对这些方法进行了全面综述，旨在帮助读者快速了解医学图像配准的发展情况。特别是，我们回顾了视网膜图像配准方面的最新进展，该领域此前未受到太多关注。此外，还讨论了视网膜图像配准当前面临的挑战，并给出了对未来研究的见解和展望。
