Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

TL;DR

This paper introduces the GlaS Challenge, a competition focused on developing automated methods for gland segmentation in colon histology images to improve reproducibility in cancer grading.

Contribution

It provides a comprehensive overview of the GlaS Challenge, including dataset, evaluation criteria, and analysis of top methods for gland segmentation.

Findings

Top methods achieved high segmentation accuracy

The dataset enabled standardized benchmarking

Automated segmentation can assist pathologists in diagnosis

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.