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

作　　者：Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu 

机构地区：[1]Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China [2]Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [3]Singapore Eye Research Institute,Singapore 169856,Singapore [4]State Key Laboratory of Ophthalmology,Optometry and Visual Science,Eye Hospital,Wenzhou Medical University,Wenzhou 325027,China

出　　处：《Visual Computing for Industry,Biomedicine,and Art》2024年第1期142-164,共23页工医艺的可视计算(英文)

基　　金：supported in part by General Program of National Natural Science Foundation of China,Nos.82102189 and 82272086;Guangdong Provincial Department of Education,No.SJZLGC202202.

摘　　要：Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion 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 med-ical 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.

关 键 词：Computer-aided diagnosis Medical image registration Deep learning Generative model TRANSFORMER RETINA 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]