Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

Yuyin Zhou; Yingwei Li; Zhishuai Zhang; Yan Wang; Angtian Wang; Elliot K. Fishman; Alan L. Yuille; Seyoun Park

doi:10.1007/978-3-030-32245-8_18

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

Yuyin Zhou^*
, Yingwei Li
, Zhishuai Zhang
, Yan Wang
, Angtian Wang
, Elliot K. Fishman
, Alan L. Yuille
, Seyoun Park

^*此作品的通讯作者

Johns Hopkins University
Huazhong University of Science and Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

61 引用（Scopus）

摘要

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

源语言	英语
主期刊名	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
编辑	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
出版商	Springer Science and Business Media Deutschland GmbH
页	155-163
页数	9
ISBN（印刷版）	9783030322441
DOI	https://doi.org/10.1007/978-3-030-32245-8_18
出版状态	已出版 - 2019
已对外发布	是
活动	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	11765 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	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

10.1007/978-3-030-32245-8_18

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhou, Y., Li, Y., Zhang, Z., Wang, Y., Wang, A., Fishman, E. K., Yuille, A. L., & Park, S. (2019). Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation. 在 D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (编辑), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (页码 155-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11765 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32245-8_18

Zhou, Yuyin ; Li, Yingwei ; Zhang, Zhishuai 等. / Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. 编辑 / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. 页码 155-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2679f1af0b2b472e8415785ef7fd7014,

title = "Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation",

abstract = "Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8\%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73\% (from 56.21\% to 63.94\%) in terms of DSC.",

author = "Yuyin Zhou and Yingwei Li and Zhishuai Zhang and Yan Wang and Angtian Wang and Fishman, \{Elliot K.\} and Yuille, \{Alan L.\} and Seyoun Park",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 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 = "10.1007/978-3-030-32245-8\_18",

language = "英语",

isbn = "9783030322441",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "155--163",

editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, \{Terry M.\} and Ali Khan and Staib, \{Lawrence H.\} and Caroline Essert and Sean Zhou",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",

address = "德国",

}

Zhou, Y, Li, Y, Zhang, Z, Wang, Y, Wang, A, Fishman, EK, Yuille, AL & Park, S 2019, Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation. 在 D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (编辑), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 11765 LNCS, Springer Science and Business Media Deutschland GmbH, 页码 155-163, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, 中国, 13/10/19. https://doi.org/10.1007/978-3-030-32245-8_18

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation. / Zhou, Yuyin; Li, Yingwei; Zhang, Zhishuai 等.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. 编辑 / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. 页码 155-163 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11765 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 - Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

AU - Zhou, Yuyin

AU - Li, Yingwei

AU - Zhang, Zhishuai

AU - Wang, Yan

AU - Wang, Angtian

AU - Fishman, Elliot K.

AU - Yuille, Alan L.

AU - Park, Seyoun

PY - 2019

Y1 - 2019

N2 - Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

AB - Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.

UR - https://www.scopus.com/pages/publications/85075677736

U2 - 10.1007/978-3-030-32245-8_18

DO - 10.1007/978-3-030-32245-8_18

M3 - 会议稿件

AN - SCOPUS:85075677736

SN - 9783030322441

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

SP - 155

EP - 163

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer Science and Business Media Deutschland GmbH

T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 17 October 2019

ER -

Zhou Y, Li Y, Zhang Z, Wang Y, Wang A, Fishman EK 等. Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation. 在 Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, 编辑, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. 页码 155-163. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32245-8_18

Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

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

学术指纹

引用此