TY - JOUR
T1 - A robust nonlinear tissue-component discrimination method for computational pathology
AU - Sarnecki, Jacob S.
AU - Burns, Kathleen H.
AU - Wood, Laura D.
AU - Waters, Kevin M.
AU - Hruban, Ralph H.
AU - Wirtz, Denis
AU - Wu, Pei Hsun
N1 - Publisher Copyright:
© 2016 USCAP, Inc All rights reserved.
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
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U2 - 10.1038/labinvest.2015.162
DO - 10.1038/labinvest.2015.162
M3 - Article
C2 - 26779829
AN - SCOPUS:84962210207
SN - 0023-6837
VL - 96
SP - 450
EP - 458
JO - Laboratory Investigation
JF - Laboratory Investigation
IS - 4
ER -