Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
This work tackles the problem of inconsistent cancer grading in pathology for clinicians by providing a benchmark for automated gland segmentation, though it is incremental as it builds on existing segmentation challenges.
The paper introduces the GlaS Challenge Contest for automated gland segmentation in colon histology images to address reproducibility issues in cancer grading, with top-performing methods evaluated on a dataset from MICCAI'2015.
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.