A robust nonlinear tissue-component discrimination method for computational pathology View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-04

AUTHORS

Jacob S Sarnecki, Kathleen H Burns, Laura D Wood, Kevin M Waters, Ralph H Hruban, Denis Wirtz, Pei-Hsun Wu

ABSTRACT

Advances in digital pathology, specifically imaging instrumentation and data management, have allowed for the development of computational pathology tools with the potential for better, faster, and cheaper diagnosis, prognosis, and prediction of disease. Images of tissue sections frequently vary in color appearance across research laboratories and medical facilities because of differences in tissue fixation, staining protocols, and imaging instrumentation, leading to difficulty in the development of robust computational tools. To address this challenge, we propose a novel nonlinear tissue-component discrimination (NLTD) method to register automatically the color space of histopathology images and visualize individual tissue components, independent of color differences between images. Our results show that the NLTD method could effectively discriminate different tissue components from different types of tissues prepared at different institutions. Further, we demonstrate that NLTD can improve the accuracy of nuclear detection and segmentation algorithms, compared with using conventional color deconvolution methods, and can quantitatively analyze immunohistochemistry images. Together, the NLTD method is objective, robust, and effective, and can be easily implemented in the emerging field of computational pathology. More... »

PAGES

450-458

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/labinvest.2015.162

DOI

http://dx.doi.org/10.1038/labinvest.2015.162

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044285694

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26779829


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