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

School of Computer Science

Northwestern Polytechnical University Xian
The University of Sydney

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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’ 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.

Original language	English
Title of host publication	Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers
Editors	Kai Yu, Yuxin Peng, Xingpeng Jiang, Jiwen Lu
Publisher	Springer Verlag
Pages	655-662
Number of pages	8
ISBN (Print)	9783030026974
DOIs	https://doi.org/10.1007/978-3-030-02698-1_57
State	Published - 2018
Event	8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018 - Lanzhou, China Duration: 18 Aug 2018 → 19 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11266 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018
Country/Territory	China
City	Lanzhou
Period	18/08/18 → 19/08/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

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

Cite this

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

Zhang, Junjie ; Huang, Zhaowei ; Huang, Tairan et al. / 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. editor / Kai Yu ; Yuxin Peng ; Xingpeng Jiang ; Jiwen Lu. Springer Verlag, 2018. pp. 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. in K Yu, Y Peng, X Jiang & J Lu (eds), 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), vol. 11266 LNCS, Springer Verlag, pp. 655-662, 8th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2018, Lanzhou, China, 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 et al.
Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers. ed. / Kai Yu; Yuxin Peng; Xingpeng Jiang; Jiwen Lu. Springer Verlag, 2018. p. 655-662 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11266 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In Yu K, Peng Y, Jiang X, Lu J, editors, Intelligence Science and Big Data Engineering - 8th International Conference, IScIDE 2018, Revised Selected Papers. Springer Verlag. 2018. p. 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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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