Research progress of computer aided diagnosis system for pulmonary nodules in CT images
文献类型：期刊论文
| 作者 | Wang, Yu1,4; Wu, Bo1,4; Zhang, Nan1,4; Liu, Jiabao2; Ren, Fei3; Zhao, Liqin2 |
| 刊名 | JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY
 |
| 出版日期 | 2020 |
| 卷号 | 28期号:1页码:1-16 |
| 关键词 | Pulmonary nodules computer-aided detection (CADe) computer-aided diagnosis (CADx) multi-task CAD |
| ISSN号 | 0895-3996 |
| DOI | 10.3233/XST-190581 |
| 英文摘要 | BACKGROUND AND OBJECTIVE: Since CAD (Computer Aided Diagnosis) system can make it easier and more efficient to interpret CT (Computer Tomography) images, it has gained much attention and developed rapidly in recent years. This article reviews recent CAD techniques for pulmonary nodule detection and diagnosis in CT Images. METHODS: CAD systems can be classified into computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. This review reports recent researches of both systems, including the database, technique, innovation and experimental results of each work. Multi-task CAD systems, which can handle segmentation, false positive reduction, malignancy prediction and other tasks at the same time. The commercial CAD systems are also briefly introduced. RESULTS: We have found that deep learning based CAD is the mainstream of current research. The reported sensitivity of deep learning based CADe systems ranged between 80.06% and 94.1% with an average 4.3 false-positive (FP) per scan when using LIDC-IDRI dataset, and between 94.4% and 97.9% with an average 4 FP/scan when using LUNA16 dataset, respectively. The overall accuracy of deep learning based CADx systems ranged between 86.84% and 92.3% with an average AUC of 0.956 reported when using LIDC-IDRI dataset. CONCLUSIONS: We summarized the current tendency and limitations as well as future challenges in this field. The development of CAD needs to meet the rigid clinical requirements, such as high accuracy, strong robustness, high efficiency, fine-grained analysis and classification, and to provide practical clinical functions. This review provides helpful information for both engineering researchers and radiologists to learn the latest development of CAD systems. |
| 资助项目 | National Natural Science Foundation of China[61672362] ; National Natural Science Foundation of China[U1611263] ; Beijing Natural Science Foundation[4172012] ; Beijing Natural Science Foundation[7192042] ; Scientific Research Common Program of Beijing Municipal Commission of Education[KM201710025011] |
| WOS研究方向 | Instruments & Instrumentation ; Optics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000516570400001 |
| 出版者 | IOS PRESS |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/14056]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhang, Nan; Liu, Jiabao |
| 作者单位 | 1.Capital Med Univ, Beijing Key Lab Fundamental Res Biomech Clin Appl, Beijing, Peoples R China 2.Capital Med Univ, Beijing Friendship Hosp, Dept Radiol, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 4.Capital Med Univ, Sch Biomed Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yu,Wu, Bo,Zhang, Nan,et al. Research progress of computer aided diagnosis system for pulmonary nodules in CT images[J]. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY,2020,28(1):1-16. |
| APA | Wang, Yu,Wu, Bo,Zhang, Nan,Liu, Jiabao,Ren, Fei,&Zhao, Liqin.(2020).Research progress of computer aided diagnosis system for pulmonary nodules in CT images.JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY,28(1),1-16. |
| MLA | Wang, Yu,et al."Research progress of computer aided diagnosis system for pulmonary nodules in CT images".JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 28.1(2020):1-16. |
入库方式： OAI收割
来源：计算技术研究所
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