Multi-class texture analysis in colorectal cancer histology.
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.
Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.|Institute of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Jakob Nikolas
JN
Kather
Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Cleo-Aron
CA
Weis
Department of Engineering, University of Perugia, Perugia, Italy.
Francesco
F
Bianconi
Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Susanne M
SM
Melchers
Institute of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Lothar R
LR
Schad
Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Timo
T
Gaiser
Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Alexander
A
Marx
Institute of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Frank Gerrit
FG
Zöllner
D057205:Automation, Laboratory / Q000379:methods*; D015179:Colorectal Neoplasms / Q000473:pathology*; D006651:Histocytochemistry / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*