Gland segmentation in colon histology images: The glas challenge contest

Korsuk Sirinukunwattana; Josien P.W. Pluim; Hao Chen; Xiaojuan Qi; Pheng Ann Heng; Yun Bo Guo; Li Yang Wang; Bogdan J. Matuszewski; Elia Bruni; Urko Sanchez; Anton Böhm; Olaf Ronneberger; Bassem Ben Cheikh; Daniel Racoceanu; Philipp Kainz; Michael Pfeiffer; Martin Urschler; David R.J. Snead; Nasir M. Rajpoot

doi:10.1016/j.media.2016.08.008

Gland segmentation in colon histology images: The glas challenge contest

Korsuk Sirinukunwattana
, Josien P.W. Pluim
, Hao Chen
, Xiaojuan Qi
, Pheng Ann Heng
, Yun Bo Guo
, Li Yang Wang
, Bogdan J. Matuszewski
, Elia Bruni
, Urko Sanchez
, Anton Böhm
, Olaf Ronneberger
, Bassem Ben Cheikh
, Daniel Racoceanu
, Philipp Kainz
, Michael Pfeiffer
, Martin Urschler
, David R.J. Snead
, Nasir M. Rajpoot

University of Warwick
Eindhoven University of Technology
Chinese University of Hong Kong
University of Central Lancashire
ExB Research and Development
University of Freiburg
Sorbonne Université
Medical University of Graz
University of Zurich
Graz University of Technology
Ludwig-Boltzmann-Inst. Urban E.
University Hospitals Coventry and Warwickshire NHS Trust

Research output: Contribution to journal › Article › peer-review

878 Scopus citations

Abstract

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI’2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Original language	English
Pages (from-to)	489-502
Number of pages	14
Journal	Medical Image Analysis
Volume	35
DOIs	https://doi.org/10.1016/j.media.2016.08.008
State	Published - 1 Jan 2017
Externally published	Yes

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Colon cancer
Digital pathology
Histology image analysis
Intestinal gland
Segmentation

Access to Document

10.1016/j.media.2016.08.008

Cite this

Sirinukunwattana, K., Pluim, J. P. W., Chen, H., Qi, X., Heng, P. A., Guo, Y. B., Wang, L. Y., Matuszewski, B. J., Bruni, E., Sanchez, U., Böhm, A., Ronneberger, O., Cheikh, B. B., Racoceanu, D., Kainz, P., Pfeiffer, M., Urschler, M., Snead, D. R. J., & Rajpoot, N. M. (2017). Gland segmentation in colon histology images: The glas challenge contest. Medical Image Analysis, 35, 489-502. https://doi.org/10.1016/j.media.2016.08.008

@article{5ac3488e99344284b630c69215b4ee50,

title = "Gland segmentation in colon histology images: The glas challenge contest",

abstract = "Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI{\textquoteright}2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.",

keywords = "Colon cancer, Digital pathology, Histology image analysis, Intestinal gland, Segmentation",

author = "Korsuk Sirinukunwattana and Pluim, \{Josien P.W.\} and Hao Chen and Xiaojuan Qi and Heng, \{Pheng Ann\} and Guo, \{Yun Bo\} and Wang, \{Li Yang\} and Matuszewski, \{Bogdan J.\} and Elia Bruni and Urko Sanchez and Anton B{\"o}hm and Olaf Ronneberger and Cheikh, \{Bassem Ben\} and Daniel Racoceanu and Philipp Kainz and Michael Pfeiffer and Martin Urschler and Snead, \{David R.J.\} and Rajpoot, \{Nasir M.\}",

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year = "2017",

month = jan,

day = "1",

doi = "10.1016/j.media.2016.08.008",

language = "English",

volume = "35",

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Sirinukunwattana, K, Pluim, JPW, Chen, H, Qi, X, Heng, PA, Guo, YB, Wang, LY, Matuszewski, BJ, Bruni, E, Sanchez, U, Böhm, A, Ronneberger, O, Cheikh, BB, Racoceanu, D, Kainz, P, Pfeiffer, M, Urschler, M, Snead, DRJ & Rajpoot, NM 2017, 'Gland segmentation in colon histology images: The glas challenge contest', Medical Image Analysis, vol. 35, pp. 489-502. https://doi.org/10.1016/j.media.2016.08.008

TY - JOUR

T1 - Gland segmentation in colon histology images

T2 - The glas challenge contest

AU - Sirinukunwattana, Korsuk

AU - Pluim, Josien P.W.

AU - Chen, Hao

AU - Qi, Xiaojuan

AU - Heng, Pheng Ann

AU - Guo, Yun Bo

AU - Wang, Li Yang

AU - Matuszewski, Bogdan J.

AU - Bruni, Elia

AU - Sanchez, Urko

AU - Böhm, Anton

AU - Ronneberger, Olaf

AU - Cheikh, Bassem Ben

AU - Racoceanu, Daniel

AU - Kainz, Philipp

AU - Pfeiffer, Michael

AU - Urschler, Martin

AU - Snead, David R.J.

AU - Rajpoot, Nasir M.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI’2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

AB - Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI’2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

KW - Colon cancer

KW - Digital pathology

KW - Histology image analysis

KW - Intestinal gland

KW - Segmentation

UR - https://www.scopus.com/pages/publications/84994310706

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DO - 10.1016/j.media.2016.08.008

M3 - Article

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AN - SCOPUS:84994310706

SN - 1361-8415

VL - 35

SP - 489

EP - 502

JO - Medical Image Analysis

JF - Medical Image Analysis

ER -

Gland segmentation in colon histology images: The glas challenge contest

Abstract

UN SDGs

Keywords

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