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

Research output: Contribution to journalArticleResearch

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 languageUndefined/Unknown
JournalarXiv.org e-Print archive
Publication statusPublished - 1 Mar 2016

Keywords

  • cs.CV

Fields of Expertise

  • Information, Communication & Computing

Cite this

Sirinukunwattana, K., Pluim, J. P. W., Chen, H., Qi, X., Heng, P-A., Guo, Y. B., ... Rajpoot, N. M. (2016). Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest. arXiv.org e-Print archive.

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.

In: arXiv.org e-Print archive, 01.03.2016.

Research output: Contribution to journalArticleResearch

Sirinukunwattana, K, Pluim, JPW, Chen, H, Qi, X, Heng, P-A, 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 2016, 'Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest' arXiv.org e-Print archive.
Sirinukunwattana K, Pluim JPW, Chen H, Qi X, Heng P-A, Guo YB et al. Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest. arXiv.org e-Print archive. 2016 Mar 1.
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. / Gland Segmentation in Colon Histology Images : The GlaS Challenge Contest. In: arXiv.org e-Print archive. 2016.
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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.",
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AU - Sirinukunwattana, Korsuk

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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

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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.

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