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

被引:3
|
作者
Nie, Qiushi [1 ,2 ]
Zhang, Xiaoqing [1 ,2 ,3 ,4 ]
Hu, Yan [1 ,2 ]
Gong, Mingdao [1 ,2 ]
Liu, Jiang [1 ,2 ,5 ,6 ]
机构
[1] Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen 518055, Peoples R China
[2] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
[3] Chinese Acad Sci, Ctr High Performance Comp, Shenzhen 518055, Peoples R China
[4] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Intelligent Bioinformat, Shenzhen 518055, Peoples R China
[5] Singapore Eye Res Inst, Singapore 169856, Singapore
[6] Wenzhou Med Univ, Eye Hosp, State Key Lab Ophthalmol Optometry & Visual Sci, Wenzhou 325027, Peoples R China
基金
中国国家自然科学基金;
关键词
Computer-aided diagnosis; Medical image registration; Deep learning; Generative model; Transformer; Retina; DIABETIC-RETINOPATHY; DEFORMABLE REGISTRATION; LEARNING FRAMEWORK; SEGMENTATION; TRANSFORMER; ALGORITHM; NETWORK; SIFT;
D O I
10.1186/s42492-024-00173-8
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
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, 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.
引用
收藏
页数:23
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