CASIA OpenIR  > 中国科学院分子影像重点实验室
Application of machine learning method in optical molecular imaging: a review
An, Yu1; Meng, Hui1,2; Gao, Yuan1,2; Tong, Tong1,2; Zhang, Chong1,2; Wang, Kun1; Tian, Jie1,3
Source PublicationSCIENCE CHINA-INFORMATION SCIENCES
ISSN1674-733X
2020
Volume63Issue:1Pages:16
Abstract

Optical molecular imaging (OMI) is an imaging technology that uses an optical signal, such as near-infrared light, to detect biological tissue in organisms. Because of its specific and sensitive imaging performance, it is applied in both preclinical research and clinical surgery. However, it requires heavy data analysis and a complex mathematical model of tomographic imaging. In recent years, machine learning (ML)-based artificial intelligence has been used in different fields because of its ability to perform powerful data processing. Its analytical capability for processing complex and large data provides a feasible scheme for the requirement of OMI. In this paper, we review ML-based methods applied in different OMI modalities.

Keywordoptical molecular imaging machine learning artificial intelligence
DOI10.1007/s11432-019-2708-1
WOS KeywordCONVOLUTIONAL NEURAL-NETWORKS ; BIOLUMINESCENCE TOMOGRAPHY ; COHERENCE TOMOGRAPHY ; PHOTOACOUSTIC TOMOGRAPHY ; RECONSTRUCTION ALGORITHM ; LUMEN SEGMENTATION ; FLUORESCENCE ; CANCER ; QUANTIFICATION ; DIAGNOSIS
Indexed BySCI
Language英语
Funding ProjectGeneral Financial Grant from the China Postdoctoral Science Foundation[2017M620952] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB01030200] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030200] ; National Natural Science Foundation of China[61901472] ; Ministry of Science and Technology of China[2016YFC0103702] ; Ministry of Science and Technology of China[2018YFC0910602] ; Ministry of Science and Technology of China[2016YFA0100902] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Ministry of Science and Technology of China[2017YFA0700401] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[GJJSTD20170004] ; National Natural Science Foundation of China[61671449] ; Ministry of Science and Technology of China[2017YFA0205200] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[61671449] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Ministry of Science and Technology of China[2017YFA0700401] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Ministry of Science and Technology of China[2016YFA0100902] ; Ministry of Science and Technology of China[2018YFC0910602] ; Ministry of Science and Technology of China[2016YFC0103702] ; National Natural Science Foundation of China[61901472] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32030200] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB01030200] ; General Financial Grant from the China Postdoctoral Science Foundation[2017M620952]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000513494600001
PublisherSCIENCE PRESS
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28599
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.Chinese Acad Sci, Beijing Key Lab Mol Imaging, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging,Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
Recommended Citation
GB/T 7714
An, Yu,Meng, Hui,Gao, Yuan,et al. Application of machine learning method in optical molecular imaging: a review[J]. SCIENCE CHINA-INFORMATION SCIENCES,2020,63(1):16.
APA An, Yu.,Meng, Hui.,Gao, Yuan.,Tong, Tong.,Zhang, Chong.,...&Tian, Jie.(2020).Application of machine learning method in optical molecular imaging: a review.SCIENCE CHINA-INFORMATION SCIENCES,63(1),16.
MLA An, Yu,et al."Application of machine learning method in optical molecular imaging: a review".SCIENCE CHINA-INFORMATION SCIENCES 63.1(2020):16.
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