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Gland segmentation in colon histology images: the GlaS challenge contest


Sirinukunwattana, Korsuk; Pluim, Josien P W; Chen, Hao; Qi, Xiaojuan; Heng, Pheng-Ann; Guo, Yun Bo; Wang, Li Yang; Matuszewski, Bogdan J; Bruni, Elia; Sanchez, Urko; Böhm, Anton; Ronneberger, Olaf; Cheikh, Bassem Ben; Racoceanu, Daniel; Kainz, Philipp; Pfeiffer, Michael; Urschler, Martin; Snead, David R J; Rajpoot, Nasir M (2016). Gland segmentation in colon histology images: the GlaS challenge contest. arXiv: Computer Science/Computer Vision and Pattern Recognition 1603.00275, Institute of Neuroinformatics.

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.

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.

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Additional indexing

Item Type:Working Paper
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2016
Deposited On:26 Jan 2017 11:48
Last Modified:29 Aug 2017 22:26
Series Name:arXiv: Computer Science/Computer Vision and Pattern Recognition
Number of Pages:24
Free access at:Official URL. An embargo period may apply.
Official URL:http://arxiv.org/abs/1603.00275

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