Incorporating Local and Global Context for Better Automated Analysis of Colorectal Cancer on Digital Pathology Slides

Phenotypic information derived from visual characteristics of colorectal cancer (CRC) is routinely used for diagnosis and recommendations for treatment. Previously published studies show that the ratio of tissue types within CRC is prognostic. Such studies generate large amounts of data, combining expert classifications with x-y coordinates, which has previously been used to train image analysis algorithms. This paper describes extensions to algorithms employed in previously published work, using pixel clustering as a pre-processing step before normalised cuts in order to reduce the size of the graph for unsupervised segmentation. Image segments are processed for features and given a candidate classification which is weighted by neighbouring segment classes. Global slide features are incorporated to mitigate inconsistencies in overall appearance caused by histological and biological differences. The proposed algorithm increases agreement with the ground truth from 75% to 79% on a dataset of 7,159 images across 157 digital slides.