Nuclei instance segmentation with dual contour-enhanced adversarial network

Donghao Zhang; Yang Song; Siqi Liu; Dagan Feng; Yue Wang; Weidong Cai

doi:10.1109/ISBI.2018.8363604

Nuclei instance segmentation with dual contour-enhanced adversarial network

Donghao Zhang, Yang Song, Siqi Liu, Dagan Feng, Yue Wang, Weidong Cai

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

15 Citations (Scopus)

Abstract

The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods.

Original language	English
Title of host publication	Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
Editors	Erik Meijering, Ron Summers
Place of Publication	USA
Publisher	IEEE Computer Society
Pages	409-412
Number of pages	4
Edition	1st
ISBN (Electronic)	9781538636367
DOIs	https://doi.org/10.1109/ISBI.2018.8363604
Publication status	Published - 2018
Externally published	Yes
Event	IEEE International Symposium on Biomedical Imaging (ISBI) 2018 - Washington, United States of America Duration: 4 Apr 2018 → 7 Apr 2018 Conference number: 15th https://ieeexplore.ieee.org/xpl/conhome/9433749/proceeding

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2018-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	IEEE International Symposium on Biomedical Imaging (ISBI) 2018
Abbreviated title	ISBI 2018
Country/Territory	United States of America
City	Washington
Period	4/04/18 → 7/04/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9433749/proceeding

Keywords

Generative Adversarial Network
Nuclei Segmentation
Pathology Image Analysis

Access to Document

10.1109/ISBI.2018.8363604

Cite this

Zhang, D., Song, Y., Liu, S., Feng, D., Wang, Y., & Cai, W. (2018). Nuclei instance segmentation with dual contour-enhanced adversarial network. In E. Meijering, & R. Summers (Eds.), Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (1st ed., pp. 409-412). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363604

Zhang, Donghao ; Song, Yang ; Liu, Siqi et al. / Nuclei instance segmentation with dual contour-enhanced adversarial network. Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. editor / Erik Meijering ; Ron Summers. 1st. ed. USA : IEEE Computer Society, 2018. pp. 409-412 (Proceedings - International Symposium on Biomedical Imaging).

@inproceedings{7602855f08fb44a5bcea03714be79b61,

title = "Nuclei instance segmentation with dual contour-enhanced adversarial network",

abstract = "The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods.",

keywords = "Generative Adversarial Network, Nuclei Segmentation, Pathology Image Analysis",

author = "Donghao Zhang and Yang Song and Siqi Liu and Dagan Feng and Yue Wang and Weidong Cai",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; IEEE International Symposium on Biomedical Imaging (ISBI) 2018, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",

year = "2018",

doi = "10.1109/ISBI.2018.8363604",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "409--412",

editor = "Erik Meijering and Ron Summers",

booktitle = "Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",

address = "United States of America",

edition = "1st",

url = "https://ieeexplore.ieee.org/xpl/conhome/9433749/proceeding",

}

Zhang, D, Song, Y, Liu, S, Feng, D, Wang, Y & Cai, W 2018, Nuclei instance segmentation with dual contour-enhanced adversarial network. in E Meijering & R Summers (eds), Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. 1st edn, Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, USA, pp. 409-412, IEEE International Symposium on Biomedical Imaging (ISBI) 2018, Washington, District of Columbia, United States of America, 4/04/18. https://doi.org/10.1109/ISBI.2018.8363604

Nuclei instance segmentation with dual contour-enhanced adversarial network. / Zhang, Donghao; Song, Yang; Liu, Siqi et al.

Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. ed. / Erik Meijering; Ron Summers. 1st. ed. USA : IEEE Computer Society, 2018. p. 409-412 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

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

TY - GEN

T1 - Nuclei instance segmentation with dual contour-enhanced adversarial network

AU - Zhang, Donghao

AU - Song, Yang

AU - Liu, Siqi

AU - Feng, Dagan

AU - Wang, Yue

AU - Cai, Weidong

N1 - Conference code: 15th

PY - 2018

Y1 - 2018

N2 - The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods.

AB - The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods.

KW - Generative Adversarial Network

KW - Nuclei Segmentation

KW - Pathology Image Analysis

UR - http://www.scopus.com/inward/record.url?scp=85048091340&partnerID=8YFLogxK

U2 - 10.1109/ISBI.2018.8363604

DO - 10.1109/ISBI.2018.8363604

M3 - Conference Paper

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T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 409

EP - 412

BT - Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018

A2 - Meijering, Erik

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PB - IEEE Computer Society

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T2 - IEEE International Symposium on Biomedical Imaging (ISBI) 2018

Y2 - 4 April 2018 through 7 April 2018

ER -

Zhang D, Song Y, Liu S, Feng D, Wang Y, Cai W. Nuclei instance segmentation with dual contour-enhanced adversarial network. In Meijering E, Summers R, editors, Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. 1st ed. USA: IEEE Computer Society. 2018. p. 409-412. (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2018.8363604

Nuclei instance segmentation with dual contour-enhanced adversarial network

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Keywords

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