A review of deep learning-based three-dimensional medical image registration methods

doi:10.21037/qims-21-175

	A review of deep learning-based three-dimensional medical image registration methods
	Xiao, Haonan 1; Teng, Xinzhi 1; Liu, Chenyang 1; Li, Tian 1; Ren, Ge 1; Yang, Ruijie 2; Shen, Dinggang3,4,5 ; Cai, Jing 1
	2021-12
发表期刊	QUANTITATIVE IMAGING IN MEDICINE AND SURGERY
ISSN	2223-4292
EISSN	2223-4306
DOI	10.21037/qims-21-175
摘要	Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. Recently, the successful implementation of deep learning (DL) in various fields has prompted many research groups to apply DL to three-dimensional (3D) medical image registration. Several of these efforts have led to promising results. This review summarized the progress made in DL-based 3D image registration over the past 5 years and identify existing challenges and potential avenues for further research. The collected studies were statistically analyzed based on the region of interest (ROI), image modality, supervision method, and registration evaluation metrics. The studies were classified into three categories: deep iterative registration, supervised registration, and unsupervised registration. The studies are thoroughly reviewed and their unique contributions are highlighted. A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. Finally, the common challenges for all categories are discussed, and potential future research topics are identified.
关键词	Artificial intelligence deep learning (DL) image registration image-guided radiotherapy (IGRT)
URL	查看原文
收录类别	SCIE
语种	英语
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000678342500001
出版者	AME PUBL CO
原始文献类型	Review; Early Access
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/127856
专题	生物医学工程学院_PI研究组_沈定刚组
通讯作者	Cai, Jing
作者单位	1.Hong Kong Polytech Univ, Dept Hlth Technol & Informat, Hong Kong, Peoples R China; 2.Peking Univ Third Hosp, Dept Radiat Oncol, Beijing, Peoples R China; 3.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China; 4.Shanghai United Imaging Intelligence Co Ltd, Shanghai, Peoples R China; 5.Korea Univ, Dept Artificial Intelligence, Seoul, South Korea
推荐引用方式 GB/T 7714	Xiao, Haonan,Teng, Xinzhi,Liu, Chenyang,et al. A review of deep learning-based three-dimensional medical image registration methods[J]. QUANTITATIVE IMAGING IN MEDICINE AND SURGERY,2021.
APA	Xiao, Haonan.,Teng, Xinzhi.,Liu, Chenyang.,Li, Tian.,Ren, Ge.,...&Cai, Jing.(2021).A review of deep learning-based three-dimensional medical image registration methods.QUANTITATIVE IMAGING IN MEDICINE AND SURGERY.
MLA	Xiao, Haonan,et al."A review of deep learning-based three-dimensional medical image registration methods".QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021).