A pulmonary nodule detection method based on residual learning and dense connection

Feng Zhang; Yutong Xie; Yong Xia; Yanning Zhang

doi:10.1007/978-3-030-33391-1_9

A pulmonary nodule detection method based on residual learning and dense connection

Feng Zhang, Yutong Xie, Yong Xia, Yanning Zhang

School of Computer Science

Northwestern Polytechnical University Xian

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

1 Scopus citations

Abstract

Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.

Original language	English
Title of host publication	Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings
Editors	Qian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le
Publisher	Springer
Pages	72-80
Number of pages	9
ISBN (Print)	9783030333904
DOIs	https://doi.org/10.1007/978-3-030-33391-1_9
State	Published - 2019
Event	1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11795 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/19 → 17/10/19

Keywords

Chest CT
Dense connection
Pulmonary nodule detection
Residual learning

UN SDGs

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

Access to Document

10.1007/978-3-030-33391-1_9

Cite this

Zhang, F., Xie, Y., Xia, Y., & Zhang, Y. (2019). A pulmonary nodule detection method based on residual learning and dense connection. In Q. Wang, F. Milletari, N. Rieke, H. V. Nguyen, B. Roysam, S. Albarqouni, M. J. Cardoso, Z. Xu, K. Kamnitsas, V. Patel, S. Jiang, K. Zhou, K. Luu, & N. Le (Eds.), Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings (pp. 72-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11795 LNCS). Springer. https://doi.org/10.1007/978-3-030-33391-1_9

Zhang, Feng ; Xie, Yutong ; Xia, Yong et al. / A pulmonary nodule detection method based on residual learning and dense connection. Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings. editor / Qian Wang ; Fausto Milletari ; Nicola Rieke ; Hien V. Nguyen ; Badri Roysam ; Shadi Albarqouni ; M. Jorge Cardoso ; Ziyue Xu ; Konstantinos Kamnitsas ; Vishal Patel ; Steve Jiang ; Kevin Zhou ; Khoa Luu ; Ngan Le. Springer, 2019. pp. 72-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c339803b0a8643ec8d09833357df5c2e,

title = "A pulmonary nodule detection method based on residual learning and dense connection",

abstract = "Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.",

keywords = "Chest CT, Dense connection, Pulmonary nodule detection, Residual learning",

author = "Feng Zhang and Yutong Xie and Yong Xia and Yanning Zhang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

year = "2019",

doi = "10.1007/978-3-030-33391-1_9",

language = "英语",

isbn = "9783030333904",

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

publisher = "Springer",

pages = "72--80",

editor = "Qian Wang and Fausto Milletari and Nicola Rieke and Nguyen, {Hien V.} and Badri Roysam and Shadi Albarqouni and Cardoso, {M. Jorge} and Ziyue Xu and Konstantinos Kamnitsas and Vishal Patel and Steve Jiang and Kevin Zhou and Khoa Luu and Ngan Le",

booktitle = "Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings",

}

Zhang, F, Xie, Y, Xia, Y & Zhang, Y 2019, A pulmonary nodule detection method based on residual learning and dense connection. in Q Wang, F Milletari, N Rieke, HV Nguyen, B Roysam, S Albarqouni, MJ Cardoso, Z Xu, K Kamnitsas, V Patel, S Jiang, K Zhou, K Luu & N Le (eds), Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11795 LNCS, Springer, pp. 72-80, 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-33391-1_9

A pulmonary nodule detection method based on residual learning and dense connection. / Zhang, Feng; Xie, Yutong; Xia, Yong et al.
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings. ed. / Qian Wang; Fausto Milletari; Nicola Rieke; Hien V. Nguyen; Badri Roysam; Shadi Albarqouni; M. Jorge Cardoso; Ziyue Xu; Konstantinos Kamnitsas; Vishal Patel; Steve Jiang; Kevin Zhou; Khoa Luu; Ngan Le. Springer, 2019. p. 72-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11795 LNCS).

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

TY - GEN

T1 - A pulmonary nodule detection method based on residual learning and dense connection

AU - Zhang, Feng

AU - Xie, Yutong

AU - Xia, Yong

AU - Zhang, Yanning

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.

AB - Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.

KW - Chest CT

KW - Dense connection

KW - Pulmonary nodule detection

KW - Residual learning

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U2 - 10.1007/978-3-030-33391-1_9

DO - 10.1007/978-3-030-33391-1_9

M3 - 会议稿件

AN - SCOPUS:85075679176

SN - 9783030333904

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

SP - 72

EP - 80

BT - Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings

A2 - Wang, Qian

A2 - Milletari, Fausto

A2 - Rieke, Nicola

A2 - Nguyen, Hien V.

A2 - Roysam, Badri

A2 - Albarqouni, Shadi

A2 - Cardoso, M. Jorge

A2 - Xu, Ziyue

A2 - Kamnitsas, Konstantinos

A2 - Patel, Vishal

A2 - Jiang, Steve

A2 - Zhou, Kevin

A2 - Luu, Khoa

A2 - Le, Ngan

PB - Springer

T2 - 1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 17 October 2019

ER -

Zhang F, Xie Y, Xia Y , Zhang Y. A pulmonary nodule detection method based on residual learning and dense connection. In Wang Q, Milletari F, Rieke N, Nguyen HV, Roysam B, Albarqouni S, Cardoso MJ, Xu Z, Kamnitsas K, Patel V, Jiang S, Zhou K, Luu K, Le N, editors, Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings. Springer. 2019. p. 72-80. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33391-1_9

A pulmonary nodule detection method based on residual learning and dense connection

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Keywords

UN SDGs

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