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

计算机学院

Northwestern Polytechnical University Xian

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	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
编辑	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
出版商	Springer
页	72-80
页数	9
ISBN（印刷版）	9783030333904
DOI	https://doi.org/10.1007/978-3-030-33391-1_9
出版状态	已出版 - 2019
活动	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, 中国期限: 13 10月 2019 → 17 10月 2019

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	11795 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	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
国家/地区	中国
市	Shenzhen
时期	13/10/19 → 17/10/19

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

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

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, F., Xie, Y., Xia, Y., & Zhang, Y. (2019). A pulmonary nodule detection method based on residual learning and dense connection. 在 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 (编辑), 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 (页码 72-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11795 LNCS). Springer. https://doi.org/10.1007/978-3-030-33391-1_9

Zhang, Feng ; Xie, Yutong ; Xia, Yong 等. / 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. 编辑 / 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. 页码 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. 在 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 (编辑), 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), 卷 11795 LNCS, Springer, 页码 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, 中国, 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 等.
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. 编辑 / 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. 页码 72-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11795 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 - 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

UR - https://www.scopus.com/pages/publications/85075679176

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. 在 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, 编辑, 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. 页码 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

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