Medical image registration and its application in retinal images: a review.

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Visual Computing for Industry Biomedicine and Art Pub Date : 2024-08-21 DOI:10.1186/s42492-024-00173-8
Qiushi Nie, Xiaoqing Zhang, Yan Hu, Mingdao Gong, Jiang Liu
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Abstract

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.

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医学图像配准及其在视网膜图像中的应用:综述。
医学影像配准能够合并不同时间、角度或模式下拍摄的不同图像信息,对疾病诊断和治疗至关重要。虽然已有多项研究对医学影像配准的发展进行了回顾,但并未对现有的医学影像配准方法进行系统总结。为此,我们从传统和基于深度学习的角度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。我们特别回顾了视网膜图像配准的最新进展,该领域尚未引起广泛关注。此外,还讨论了视网膜图像配准目前面临的挑战,并对未来研究提出了见解和展望。
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A study on the influence of situations on personal avatar characteristics. Noise suppression in photon-counting computed tomography using unsupervised Poisson flow generative models. Machine learning approach for the prediction of macrosomia. Medical image registration and its application in retinal images: a review. IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models.
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