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

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

Institute of Computer Graphics and Vision (7100)

Research output: Working paper › Preprint

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
Number of pages	28
Publication status	Published - 1 Mar 2016

Publication series

Name	arXiv.org e-Print archive
Publisher	Cornell University Library

Keywords

cs.CV

Fields of Expertise

Information, Communication & Computing

UN SDGs

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

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1603.00275v2Submitted manuscript, 4.97 MBLicence: CC BY-NC-ND 4.0

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Project: Research project

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. (2016). Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest. (arXiv.org e-Print archive).

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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'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 = "cs.CV",

author = "Korsuk Sirinukunwattana and Pluim, {Josien P. W.} and Hao Chen and Xiaojuan Qi and Pheng-Ann Heng 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.}",

year = "2016",

month = mar,

day = "1",

language = "English",

series = "arXiv.org e-Print archive",

publisher = "Cornell University Library",

type = "WorkingPaper",

institution = "Cornell University Library",

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TY - UNPB

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 - 2016/3/1

Y1 - 2016/3/1

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 - cs.CV

M3 - Preprint

T3 - arXiv.org e-Print archive

BT - Gland Segmentation in Colon Histology Images

ER -

Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Abstract

Publication series

Keywords

Fields of Expertise

UN SDGs

Access to Document

Projects

FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents

Cite this