Rajpoot, Nasir M. and Sirinukunwattana, Korsuk (2017) Gland segmentation in colon histology images : the GlaS challenge contest. Medical Image Analysis, 35 . pp. 489-502. doi:10.1016/j.media.2016.08.008 ISSN 1361-8415.

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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.

Item Type:	Journal Article
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Colon (Anatomy) -- Cancer -- Diagnosis , Rectum -- Cancer -- Diagnosis , Colon (Anatomy) -- Cancer -- Treatment, Rectum -- Cancer -- Treatment, Histology
Journal or Publication Title:	Medical Image Analysis
Publisher:	Elsevier Science BV
ISSN:	1361-8415
Official Date:	January 2017
Dates:	Date Event January 2017 Published 2 September 2016 Available 23 August 2016 Accepted 26 February 2016 Submitted
Volume:	35
Page Range:	pp. 489-502
DOI:	10.1016/j.media.2016.08.008
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	2 September 2016
Date of first compliant Open Access:	2 September 2017
Funder:	Qatar National Research Fund (QNRF), University of Warwick. Department of Computer Science, Chinese University of Hong Kong (CUHK), Deutschland Bundesregierung, Vereinigung der Österreichischen Industrie, Fonds zur Förderung der Wissenschaftlichen Forschung (Austria) (FWF)
Grant number:	NPRP5-1345-1-228 (QNRF), 412513 (CUHK), EXC 294 (Deutschland Bundesregierung), Excellence Grant 2014 (Vereinigung der Österreichischen Industrie), P28078-N33 (FWF)

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