Lung nodule classification by jointly using visual descriptors and deep features

Yutong Xie; Jianpeng Zhang; Sidong Liu; Weidong Cai; Yong Xia

doi:10.1007/978-3-319-61188-4_11

Lung nodule classification by jointly using visual descriptors and deep features

Yutong Xie
, Jianpeng Zhang
, Sidong Liu
, Weidong Cai
, Yong Xia

School of Computer Science

Northwestern Polytechnical University Xian
The University of Sydney

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

33 Scopus citations

Abstract

Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.

Original language	English
Title of host publication	Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
Editors	Tal Arbel, Georg Langs, Mark Jenkinson, Bjoern Menze, William M. Wells III, Albert C.S. Chung, B. Michael Kelm, Weidong Cai, Albert Montillo, Dimitris Metaxas, M. Jorge Cardoso, Shaoting Zhang, Annemie Ribbens, Henning Muller
Publisher	Springer Verlag
Pages	116-125
Number of pages	10
ISBN (Print)	9783319611877
DOIs	https://doi.org/10.1007/978-3-319-61188-4_11
State	Published - 2017
Event	International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 21 Oct 2016 → 21 Oct 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10081 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	21/10/16 → 21/10/16

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Computed tomography
Deep convolutional neural network
Lung nodule classification
Shape descriptor
Texture descriptor

Access to Document

10.1007/978-3-319-61188-4_11

Cite this

Xie, Y., Zhang, J., Liu, S., Cai, W., & Xia, Y. (2017). Lung nodule classification by jointly using visual descriptors and deep features. In T. Arbel, G. Langs, M. Jenkinson, B. Menze, W. M. Wells III, A. C. S. Chung, B. M. Kelm, W. Cai, A. Montillo, D. Metaxas, M. J. Cardoso, S. Zhang, A. Ribbens, & H. Muller (Eds.), Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers (pp. 116-125). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61188-4_11

Xie, Yutong ; Zhang, Jianpeng ; Liu, Sidong et al. / Lung nodule classification by jointly using visual descriptors and deep features. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. editor / Tal Arbel ; Georg Langs ; Mark Jenkinson ; Bjoern Menze ; William M. Wells III ; Albert C.S. Chung ; B. Michael Kelm ; Weidong Cai ; Albert Montillo ; Dimitris Metaxas ; M. Jorge Cardoso ; Shaoting Zhang ; Annemie Ribbens ; Henning Muller. Springer Verlag, 2017. pp. 116-125 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2ff93aaae4dd46279cadec7f9f64faef,

title = "Lung nodule classification by jointly using visual descriptors and deep features",

abstract = "Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.",

keywords = "Computed tomography, Deep convolutional neural network, Lung nodule classification, Shape descriptor, Texture descriptor",

author = "Yutong Xie and Jianpeng Zhang and Sidong Liu and Weidong Cai and Yong Xia",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

year = "2017",

doi = "10.1007/978-3-319-61188-4\_11",

language = "英语",

isbn = "9783319611877",

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

publisher = "Springer Verlag",

pages = "116--125",

editor = "Tal Arbel and Georg Langs and Mark Jenkinson and Bjoern Menze and \{Wells III\}, \{William M.\} and Chung, \{Albert C.S.\} and Kelm, \{B. Michael\} and Weidong Cai and Albert Montillo and Dimitris Metaxas and Cardoso, \{M. Jorge\} and Shaoting Zhang and Annemie Ribbens and Henning Muller",

booktitle = "Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers",

}

Xie, Y, Zhang, J, Liu, S, Cai, W & Xia, Y 2017, Lung nodule classification by jointly using visual descriptors and deep features. in T Arbel, G Langs, M Jenkinson, B Menze, WM Wells III, ACS Chung, BM Kelm, W Cai, A Montillo, D Metaxas, MJ Cardoso, S Zhang, A Ribbens & H Muller (eds), Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10081 LNCS, Springer Verlag, pp. 116-125, International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 21/10/16. https://doi.org/10.1007/978-3-319-61188-4_11

Lung nodule classification by jointly using visual descriptors and deep features. / Xie, Yutong; Zhang, Jianpeng; Liu, Sidong et al.
Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. ed. / Tal Arbel; Georg Langs; Mark Jenkinson; Bjoern Menze; William M. Wells III; Albert C.S. Chung; B. Michael Kelm; Weidong Cai; Albert Montillo; Dimitris Metaxas; M. Jorge Cardoso; Shaoting Zhang; Annemie Ribbens; Henning Muller. Springer Verlag, 2017. p. 116-125 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS).

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

TY - GEN

T1 - Lung nodule classification by jointly using visual descriptors and deep features

AU - Xie, Yutong

AU - Zhang, Jianpeng

AU - Liu, Sidong

AU - Cai, Weidong

AU - Xia, Yong

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.

AB - Classifying benign and malignant lung nodules using the thoracic computed tomography (CT) screening is the primary method for early diagnosis of lung cancer. Despite of their widely recognized success in image classification, deep learning techniques may not achieve satisfying accuracy on this problem, due to the limited training samples resulted from the all-consuming nature of medical image acquisition and annotation. In this paper, we jointly use the texture and shape descriptors, which characterize the heterogeneity of nodules, and the features learned by a deep convolutional neural network, and thus proposed a combined-feature based classification (CFBC) algorithm to differentiate lung nodules. We have evaluated this algorithm against four state-of-the-art nodule classification approaches on the benchmark LIDC-IDRI dataset. Our results suggest that the proposed CFBC algorithm can distinguish malignant lung nodules from benign ones more accurately than other four methods.

KW - Computed tomography

KW - Deep convolutional neural network

KW - Lung nodule classification

KW - Shape descriptor

KW - Texture descriptor

UR - https://www.scopus.com/pages/publications/85025153028

U2 - 10.1007/978-3-319-61188-4_11

DO - 10.1007/978-3-319-61188-4_11

M3 - 会议稿件

AN - SCOPUS:85025153028

SN - 9783319611877

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

SP - 116

EP - 125

BT - Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers

A2 - Arbel, Tal

A2 - Langs, Georg

A2 - Jenkinson, Mark

A2 - Menze, Bjoern

A2 - Wells III, William M.

A2 - Chung, Albert C.S.

A2 - Kelm, B. Michael

A2 - Cai, Weidong

A2 - Montillo, Albert

A2 - Metaxas, Dimitris

A2 - Cardoso, M. Jorge

A2 - Zhang, Shaoting

A2 - Ribbens, Annemie

A2 - Muller, Henning

PB - Springer Verlag

T2 - International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

Y2 - 21 October 2016 through 21 October 2016

ER -

Xie Y, Zhang J, Liu S, Cai W, Xia Y. Lung nodule classification by jointly using visual descriptors and deep features. In Arbel T, Langs G, Jenkinson M, Menze B, Wells III WM, Chung ACS, Kelm BM, Cai W, Montillo A, Metaxas D, Cardoso MJ, Zhang S, Ribbens A, Muller H, editors, Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Springer Verlag. 2017. p. 116-125. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61188-4_11

Lung nodule classification by jointly using visual descriptors and deep features

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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