Gland segmentation in colon histology images: The glas challenge contest

• Published in: Medical image analysis 2017-01, Vol.35, p.489-502
• Main Authors: 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.
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Algorithms; Artificial Intelligence; Automation; Cancer; Clinical medicine; Colon; Colon cancer; Colonic Neoplasms - diagnostic imaging; Colonic Neoplasms - pathology; Colorectal cancer; Computer Science; Computer Vision and Pattern Recognition; Datasets as Topic; Diagnostic Imaging - methods; Digital pathology; Engineering Sciences; Evaluation; Glands; Histological Techniques; Histology; Histology image analysis; Humans; Image processing; Image segmentation; Intestinal gland; Intestine; Life Sciences; Medical Imaging; Medical prognosis; Morphology; Pathology; Reproducibility; Reproducibility of Results; Segmentation; Signal and Image processing
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2016.08.008

•The Gland Segmentation in Colon Histology Images Challenge (GlaS) Contest at MICCAI15.•The complete details of the challenge are presented.•The descriptions of the top performing methods are presented.•Evaluation results of the top performing methods are presented. [Display omitted] 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.