Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

By N. Linder, Juho Konsti, Riku Turkki, Esa Rahtu, M. Lundin, S. Nordling, C. Haglund, Timo Ahonen, Matti Pietikäinen, J. Lundin. 2012

157 citations recorded by the source corpus.

BackgroundThe aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.ResultsThe proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, P < 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.ConclusionsThe method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537

Subfields: Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Multi-class texture analysis in colorectal cancer histology (2016, methodology): “To our knowledge, this accuracy is higher than previously reported accuracies for similar problems – see for instance refs 8,12.”
Machine Learning Methods for Histopathological Image Analysis (2017, background): “[64] Linder N, Konsti J, Turkki R, Rahtu E, Lundin M, Nordling S, et al.”
An appendix to "Texture databases - A comprehensive survey" (2014, background): “The database is provided by the ) each class includes three different subclasses (grades) with minimal difference from each other (López et al., 2008).”
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images (2016, methodology): “In [19], local binary pattern (LBP) and contrast measure based texture features were used for discriminating epithelium and stroma from immunohistochemistry (IHC) stained tumor tissue microarrays (TMAs) of colorectal cancer.”
Discrimination between tumour epithelium and stroma via perception-based features (2015, methodology): “each classifier was tested on an independent set of 720 images of epithelium (n = 425) and stroma (n = 295) [12].”
Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “, Transfer Component Analysis (TCA) [67], Correlation Alignment (CORAL) [68], and Balanced Distribution Adaptation (BDA) [69] for breast cancer classification, and provide some”
Image analysis and machine learning in digital pathology: Challenges and opportunities (2016, unknown): “(Linder et al., 2012) employed a texture approach for segmentation of epithelium and stromal tissue partitions from tissue microarray (TMA) images.”
Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform (2014, background): “Among the numerous application areas, cooccurrence matrices seem to be particularly common in medical image analysis (Hu et al., 2012; Linder et al., 2012; Barwad et al., 2012; Parekh, 2012; Gómez et al., 2012) and remote sensing (Berthelot et al.”