南方科技大学知识苑(SUSTech KC): A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

题名	A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis
作者	Zhou，Longxi 1; Li，Zhongxiao 1; Zhou，Juexiao 2; Li，Haoyang 3; Chen，Yupeng 4; Huang，Yuxin 5; Xie，Dexuan 6; Zhao，Lintao 7; Fan，Ming 8; Hashmi，Shahrukh 9; Abdelkareem，Faisal 10; Eiada，Riham 10; Xiao，Xigang 6; Li，Lihua 8; Qiu，Zhaowen 11; Gao，Xin 1
通讯作者	Gao，Xin
发表日期	2020-08
DOI	10.1109/TMI.2020.3001810
发表期刊	IEEE Transactions on Medical Imaging 影响因子和分区
ISSN	1558-254X
卷号	39 期号:8页码:2638-2652
关键词	COVID-19 deep learning segmentation computerized tomography
相关链接	[IEEE记录]
收录类别	SCI ; EI
学校署名	其他
EI入藏号	20203409079465
EI主题词	Computer aided diagnosis ; Computerized tomography ; Learning algorithms ; Deep learning ; Nucleic acids ; Three dimensional computer graphics
EI分类号	Biomedical Engineering:461.1 ; Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Health Care:461.7 ; Data Processing and Image Processing:723.2 ; Machine Learning:723.4.2 ; Computer Applications:723.5 ; Biochemistry:801.2 ; Organic Compounds:804.1
ESI学科分类	CLINICAL MEDICINE
来源库	IEEE
全文链接	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9115057
引用统计	被引频次[WOS]：121
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/187940
专题	生命科学学院_生物系生命科学学院
作者单位	1.Computer,Electrical and Mathematical Sciences and Engineering (CEMSE) Division,Computational Bioscience Research Center (CBRC),King Abdullah University of Science and Technology (KAUST),Thuwal,Saudi Arabia 2.Department of Biology,Southern University of Science and Technology,Shenzhen,China 3.Cancer Systems Biology Center,China-Japan Union Hospital,Jilin University,Changchun,China 4.Peng Cheng Laboratory,Shenzhen,China 5.Heilongjiang Tuomeng Technology Company Ltd.,Harbin,China 6.Department of Computer Tomography,First Affiliated Hospital of Harbin Medical University,Harbin,China 7.Department of Computer Tomography,First Hospital of Harbin Medical University,Harbin,China 8.Institute of Biomedical Engineering and Instrumentation,Hangzhou Dianzi University,Hangzhou,China 9.Oncology Center,King Faisal Specialist Hospital and Research Center,Riyadh,Saudi Arabia 10.Department Medical Imaging,King Faisal Specialist Hospital and Research Center,Riyadh,Saudi Arabia 11.Institute of Information and Computer Engineering,Northeast Forestry University,Harbin,China
推荐引用方式 GB/T 7714	Zhou，Longxi,Li，Zhongxiao,Zhou，Juexiao,et al. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis[J]. IEEE Transactions on Medical Imaging,2020,39(8):2638-2652.
APA	Zhou，Longxi.,Li，Zhongxiao.,Zhou，Juexiao.,Li，Haoyang.,Chen，Yupeng.,...&Gao，Xin.(2020).A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.IEEE Transactions on Medical Imaging,39(8),2638-2652.
MLA	Zhou，Longxi,et al."A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis".IEEE Transactions on Medical Imaging 39.8(2020):2638-2652.