Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Images: Effect on Observer Performance

doi:10.1166/jmihi.2017.2201

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Images: Effect on Observer Performance
	Liu, JiaBao 1; Wang, Yu 4; Zhang, Fa 3; Ren, Fei 2; Liu, LiHeng 1; He, Wen 1
刊名	JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
	2017-10-01
卷号	7 期号:6 页码:1205-1211
关键词	Computer-Aided Detection Model Learning Lung Nodule Computed Tomography
ISSN号	2156-7018
DOI	10.1166/jmihi.2017.2201
英文摘要	Aim: To evaluate how computer-aided detection (CAD) affects observer performance in detecting lung nodules on computed tomography (CT) scans. Methods: Two hundred chest CT scans of healthy people and 80 patients' CT scans containing 96 lung nodules were retrospectively included. The CAD technique is based on sparse non-negative matrix factorization (NMF) model learning. Six observers, including two senior chest radiologists, two secondary chest radiologists and two junior radiology residents, were asked to find out the potential lung nodules on the CT scans, first without and subsequently with the assist of CAD scheme. McNemar's test was used to compare observer sensitivity without and with CAD. Results: Of the 96 nodules contained within these scans, 89 (92.7%) nodules were correctly detected by the computer, with an average 0.09 FP (false positive) annotations per CT scan. With use of the CAD scheme, the average sensitivity improved from 87.3% to 96.9% for the 6 radiologists, from 77.6% to 94.8% for junior radiology residents, from 89.1% to 97.9% for secondary chest radiologists, and from 95.3% to 97.9% for senior chest radiologists. The sensitivities of all the observers increased after reviewing the CAD annotations, however only the difference of observer D, E and F were statistically significant (p = 0.022, 0.008, < 0.001, respectively). Conclusion: Our study suggests that the CAD system can improve observer sensitivity for the detection of lung nodules in CT images.
资助项目	National Natural Science Foundation of China[61232001] ; National Natural Science Foundation of China[61502455] ; National Natural Science Foundation of China[61472397] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB08030202]
WOS研究方向	Mathematical & Computational Biology ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	AMER SCIENTIFIC PUBLISHERS
WOS记录号	WOS:000412167300012
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/6749]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Yu; He, Wen
作者单位	1.Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing 100050, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Capital Med Univ, Sch Biomed Engn, Beijing 100069, Peoples R China
推荐引用方式 GB/T 7714	Liu, JiaBao,Wang, Yu,Zhang, Fa,et al. Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Images: Effect on Observer Performance[J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS,2017,7(6):1205-1211.
APA	Liu, JiaBao,Wang, Yu,Zhang, Fa,Ren, Fei,Liu, LiHeng,&He, Wen.(2017).Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Images: Effect on Observer Performance.JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS,7(6),1205-1211.
MLA	Liu, JiaBao,et al."Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Images: Effect on Observer Performance".JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 7.6(2017):1205-1211.