Adversarial Nuclei Segmentation on HE Stained Histopathology Images

Onur Can Koyun; Tulay Yildirim

doi:10.1109/INISTA.2019.8778369

Adversarial Nuclei Segmentation on HE Stained Histopathology Images

Onur Can Koyun, Tulay Yildirim

Yildiz Technical University

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

8 Citations (Scopus)

Abstract

Computer aided methods in pathology are advancing rapidly. Problems like segmentation, classification and detection of pathology images are solved with machine learning and image processing techniques. State-of-the-art methods in nuclei segmentation problem include supervised deep learning techniques. However, labeling process of pathology images is an expensive and time consuming process. In this work, nuclei segmentation problem is formulated as image-to-image translation problem and using Cycle-Consistent Generative Adversarial Networks, an unsupervised segmentation scheme is proposed for hematoxylineosin stained histopathology data.

Original language	English
Title of host publication	IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings
Editors	Petia Koprinkova-Hristova, Tuly Yildirim, Vincenzo Piuri, Lazaros Iliadis, David Camacho
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728118628
DOIs	https://doi.org/10.1109/INISTA.2019.8778369
Publication status	Published - Jul 2019
Externally published	Yes
Event	2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Sofia, Bulgaria Duration: 3 Jul 2019 → 5 Jul 2019

Publication series

Name	IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings

Conference

Conference	2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019
Country/Territory	Bulgaria
City	Sofia
Period	3/07/19 → 5/07/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Adversary
Cell
Cycle
GAN
Nuclei
pathology
Segmentation
Unsupervised

Access to Document

10.1109/INISTA.2019.8778369

Cite this

Koyun, O. C., & Yildirim, T. (2019). Adversarial Nuclei Segmentation on HE Stained Histopathology Images. In P. Koprinkova-Hristova, T. Yildirim, V. Piuri, L. Iliadis, & D. Camacho (Eds.), IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings Article 8778369 (IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INISTA.2019.8778369

Koyun, Onur Can ; Yildirim, Tulay. / Adversarial Nuclei Segmentation on HE Stained Histopathology Images. IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings. editor / Petia Koprinkova-Hristova ; Tuly Yildirim ; Vincenzo Piuri ; Lazaros Iliadis ; David Camacho. Institute of Electrical and Electronics Engineers Inc., 2019. (IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings).

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title = "Adversarial Nuclei Segmentation on HE Stained Histopathology Images",

abstract = "Computer aided methods in pathology are advancing rapidly. Problems like segmentation, classification and detection of pathology images are solved with machine learning and image processing techniques. State-of-the-art methods in nuclei segmentation problem include supervised deep learning techniques. However, labeling process of pathology images is an expensive and time consuming process. In this work, nuclei segmentation problem is formulated as image-to-image translation problem and using Cycle-Consistent Generative Adversarial Networks, an unsupervised segmentation scheme is proposed for hematoxylineosin stained histopathology data.",

keywords = "Adversary, Cell, Cycle, GAN, Nuclei, pathology, Segmentation, Unsupervised",

author = "Koyun, {Onur Can} and Tulay Yildirim",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 ; Conference date: 03-07-2019 Through 05-07-2019",

year = "2019",

month = jul,

doi = "10.1109/INISTA.2019.8778369",

language = "English",

series = "IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Petia Koprinkova-Hristova and Tuly Yildirim and Vincenzo Piuri and Lazaros Iliadis and David Camacho",

booktitle = "IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings",

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Koyun, OC & Yildirim, T 2019, Adversarial Nuclei Segmentation on HE Stained Histopathology Images. in P Koprinkova-Hristova, T Yildirim, V Piuri, L Iliadis & D Camacho (eds), IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings., 8778369, IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019, Sofia, Bulgaria, 3/07/19. https://doi.org/10.1109/INISTA.2019.8778369

Adversarial Nuclei Segmentation on HE Stained Histopathology Images. / Koyun, Onur Can; Yildirim, Tulay.
IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings. ed. / Petia Koprinkova-Hristova; Tuly Yildirim; Vincenzo Piuri; Lazaros Iliadis; David Camacho. Institute of Electrical and Electronics Engineers Inc., 2019. 8778369 (IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings).

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

TY - GEN

T1 - Adversarial Nuclei Segmentation on HE Stained Histopathology Images

AU - Koyun, Onur Can

AU - Yildirim, Tulay

PY - 2019/7

Y1 - 2019/7

N2 - Computer aided methods in pathology are advancing rapidly. Problems like segmentation, classification and detection of pathology images are solved with machine learning and image processing techniques. State-of-the-art methods in nuclei segmentation problem include supervised deep learning techniques. However, labeling process of pathology images is an expensive and time consuming process. In this work, nuclei segmentation problem is formulated as image-to-image translation problem and using Cycle-Consistent Generative Adversarial Networks, an unsupervised segmentation scheme is proposed for hematoxylineosin stained histopathology data.

AB - Computer aided methods in pathology are advancing rapidly. Problems like segmentation, classification and detection of pathology images are solved with machine learning and image processing techniques. State-of-the-art methods in nuclei segmentation problem include supervised deep learning techniques. However, labeling process of pathology images is an expensive and time consuming process. In this work, nuclei segmentation problem is formulated as image-to-image translation problem and using Cycle-Consistent Generative Adversarial Networks, an unsupervised segmentation scheme is proposed for hematoxylineosin stained histopathology data.

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KW - Cell

KW - Cycle

KW - GAN

KW - Nuclei

KW - pathology

KW - Segmentation

KW - Unsupervised

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M3 - Conference contribution

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BT - IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings

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A2 - Yildirim, Tuly

A2 - Piuri, Vincenzo

A2 - Iliadis, Lazaros

A2 - Camacho, David

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Koyun OC, Yildirim T. Adversarial Nuclei Segmentation on HE Stained Histopathology Images. In Koprinkova-Hristova P, Yildirim T, Piuri V, Iliadis L, Camacho D, editors, IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8778369. (IEEE International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2019 - Proceedings). doi: 10.1109/INISTA.2019.8778369

Adversarial Nuclei Segmentation on HE Stained Histopathology Images

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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