Lung nodule detection using combined traditional and deep models and chest CT

Junjie Zhang; Zhaowei Huang; Tairan Huang; Yong Xia; Yanning Zhang

doi:10.1007/978-3-030-02698-1_57

Lung nodule detection using combined traditional and deep models and chest CT

Junjie Zhang
, Zhaowei Huang
, Tairan Huang
, Yong Xia
, Yanning Zhang

计算机学院

Northwestern Polytechnical University Xian
The University of Sydney

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

Detection of lung nodules in chest CT scans is of great value to the early diagnosis of lung cancer. In this paper, we jointly use traditional object detection methods and deep learning, and thus propose a lung nodule detection algorithm for chest CT scans. We first detect all candidate nodules using multi-scale Laplace of Gaussian (LoG) filters and shape priors, and finally construct a multi-scale 3D DCNN to differentiate nodules from non-nodule volumes and estimate nodules’ diameters simultaneously. This algorithm has been evaluated on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved an average diameter estimation error of 0.98 mm and a detection score of 0.913. Our results suggest that the proposed algorithm can effectively detect lung nodules on chest CT scans and accurately estimate their diameters.

源语言	英语
主期刊名	Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers
编辑	Kai Yu, Yuxin Peng, Xingpeng Jiang, Jiwen Lu
出版商	Springer Verlag
页	655-662
页数	8
ISBN（印刷版）	9783030026974
DOI	https://doi.org/10.1007/978-3-030-02698-1_57
出版状态	已出版 - 2018
活动	8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018 - Lanzhou, 中国期限: 18 8月 2018 → 19 8月 2018

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	11266 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018
国家/地区	中国
市	Lanzhou
时期	18/08/18 → 19/08/18

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

10.1007/978-3-030-02698-1_57

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, J., Huang, Z., Huang, T., Xia, Y., & Zhang, Y. (2018). Lung nodule detection using combined traditional and deep models and chest CT. 在 K. Yu, Y. Peng, X. Jiang, & J. Lu (编辑), Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers (页码 655-662). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11266 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-02698-1_57

Zhang, Junjie ; Huang, Zhaowei ; Huang, Tairan 等. / Lung nodule detection using combined traditional and deep models and chest CT. Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers. 编辑 / Kai Yu ; Yuxin Peng ; Xingpeng Jiang ; Jiwen Lu. Springer Verlag, 2018. 页码 655-662 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{78d1efa50d484e59b6c215d2c180fced,

title = "Lung nodule detection using combined traditional and deep models and chest CT",

abstract = "Detection of lung nodules in chest CT scans is of great value to the early diagnosis of lung cancer. In this paper, we jointly use traditional object detection methods and deep learning, and thus propose a lung nodule detection algorithm for chest CT scans. We first detect all candidate nodules using multi-scale Laplace of Gaussian (LoG) filters and shape priors, and finally construct a multi-scale 3D DCNN to differentiate nodules from non-nodule volumes and estimate nodules{\textquoteright} diameters simultaneously. This algorithm has been evaluated on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved an average diameter estimation error of 0.98 mm and a detection score of 0.913. Our results suggest that the proposed algorithm can effectively detect lung nodules on chest CT scans and accurately estimate their diameters.",

keywords = "Chest CT, Deep convolutional neural network (DCNN), Deep learning, Laplacian of Gaussian (LoG), Lung nodule detection",

author = "Junjie Zhang and Zhaowei Huang and Tairan Huang and Yong Xia and Yanning Zhang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018 ; Conference date: 18-08-2018 Through 19-08-2018",

year = "2018",

doi = "10.1007/978-3-030-02698-1\_57",

language = "英语",

isbn = "9783030026974",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "655--662",

editor = "Kai Yu and Yuxin Peng and Xingpeng Jiang and Jiwen Lu",

booktitle = "Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers",

}

Zhang, J, Huang, Z, Huang, T, Xia, Y & Zhang, Y 2018, Lung nodule detection using combined traditional and deep models and chest CT. 在 K Yu, Y Peng, X Jiang & J Lu (编辑), Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 11266 LNCS, Springer Verlag, 页码 655-662, 8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018, Lanzhou, 中国, 18/08/18. https://doi.org/10.1007/978-3-030-02698-1_57

Lung nodule detection using combined traditional and deep models and chest CT. / Zhang, Junjie; Huang, Zhaowei; Huang, Tairan 等.
Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers. 编辑 / Kai Yu; Yuxin Peng; Xingpeng Jiang; Jiwen Lu. Springer Verlag, 2018. 页码 655-662 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11266 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Lung nodule detection using combined traditional and deep models and chest CT

AU - Zhang, Junjie

AU - Huang, Zhaowei

AU - Huang, Tairan

AU - Xia, Yong

AU - Zhang, Yanning

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Detection of lung nodules in chest CT scans is of great value to the early diagnosis of lung cancer. In this paper, we jointly use traditional object detection methods and deep learning, and thus propose a lung nodule detection algorithm for chest CT scans. We first detect all candidate nodules using multi-scale Laplace of Gaussian (LoG) filters and shape priors, and finally construct a multi-scale 3D DCNN to differentiate nodules from non-nodule volumes and estimate nodules’ diameters simultaneously. This algorithm has been evaluated on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved an average diameter estimation error of 0.98 mm and a detection score of 0.913. Our results suggest that the proposed algorithm can effectively detect lung nodules on chest CT scans and accurately estimate their diameters.

AB - Detection of lung nodules in chest CT scans is of great value to the early diagnosis of lung cancer. In this paper, we jointly use traditional object detection methods and deep learning, and thus propose a lung nodule detection algorithm for chest CT scans. We first detect all candidate nodules using multi-scale Laplace of Gaussian (LoG) filters and shape priors, and finally construct a multi-scale 3D DCNN to differentiate nodules from non-nodule volumes and estimate nodules’ diameters simultaneously. This algorithm has been evaluated on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved an average diameter estimation error of 0.98 mm and a detection score of 0.913. Our results suggest that the proposed algorithm can effectively detect lung nodules on chest CT scans and accurately estimate their diameters.

KW - Chest CT

KW - Deep convolutional neural network (DCNN)

KW - Deep learning

KW - Laplacian of Gaussian (LoG)

KW - Lung nodule detection

UR - https://www.scopus.com/pages/publications/85057102897

U2 - 10.1007/978-3-030-02698-1_57

DO - 10.1007/978-3-030-02698-1_57

M3 - 会议稿件

AN - SCOPUS:85057102897

SN - 9783030026974

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 655

EP - 662

BT - Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers

A2 - Yu, Kai

A2 - Peng, Yuxin

A2 - Jiang, Xingpeng

A2 - Lu, Jiwen

PB - Springer Verlag

T2 - 8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018

Y2 - 18 August 2018 through 19 August 2018

ER -

Zhang J, Huang Z, Huang T, Xia Y , Zhang Y. Lung nodule detection using combined traditional and deep models and chest CT. 在 Yu K, Peng Y, Jiang X, Lu J, 编辑, Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers. Springer Verlag. 2018. 页码 655-662. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-02698-1_57

Lung nodule detection using combined traditional and deep models and chest CT

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此