Multi-class texture analysis in colorectal cancer histology

TLDR

Automatic recognition of tissue types in histological images is essential, yet texture analysis has mainly focused on tumour‑stroma ratios, leaving multiclass problems—especially for colorectal cancer—largely unexplored. This study introduces a publicly available dataset of 5,000 colorectal cancer histology images containing eight distinct tissue types to serve as a benchmark for multiclass texture analysis. We evaluated a wide range of texture descriptors and classifiers on this dataset to determine optimal classification strategies. The optimal strategy achieved 98.6 % accuracy for tumour‑stroma separation and 87.4 % accuracy across eight classes, markedly surpassing previous methods.

Abstract

Abstract Automatic recognition of different tissue types in histological images is an essential part in the digital pathology toolbox. Texture analysis is commonly used to address this problem; mainly in the context of estimating the tumour/stroma ratio on histological samples. However, although histological images typically contain more than two tissue types, only few studies have addressed the multi-class problem. For colorectal cancer, one of the most prevalent tumour types, there are in fact no published results on multiclass texture separation. In this paper we present a new dataset of 5,000 histological images of human colorectal cancer including eight different types of tissue. We used this set to assess the classification performance of a wide range of texture descriptors and classifiers. As a result, we found an optimal classification strategy that markedly outperformed traditional methods, improving the state of the art for tumour-stroma separation from 96.9% to 98.6% accuracy and setting a new standard for multiclass tissue separation (87.4% accuracy for eight classes). We make our dataset of histological images publicly available under a Creative Commons license and encourage other researchers to use it as a benchmark for their studies.