A semi-automated technique for adenoma quantification in the Apc^Min mouse using FeatureCounter.

ABSTRACT: Colorectal cancer is a major contributor to death and disease worldwide. The Apc^Min mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine of Apc^Min mice, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivit

SUBMITTER: Shepherd AL 

PROVIDER: S-EPMC7033248 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature