title: A robust nonlinear tissue-component discrimination method for computational pathology.
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.
authors.affiliation: Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD, USA.|Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Jacob S
authors.initials: JS
authors.lastname: Sarnecki
authors.affiliation: The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Kathleen H
authors.initials: KH
authors.lastname: Burns
authors.affiliation: The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Laura D
authors.initials: LD
authors.lastname: Wood
authors.affiliation: The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Waters
authors.affiliation: The Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.|Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Ralph H
authors.initials: RH
authors.lastname: Hruban
authors.affiliation: Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD, USA.|Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.|Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: Denis
authors.initials: D
authors.lastname: Wirtz
authors.affiliation: Johns Hopkins Physical Sciences-Oncology Center, The Johns Hopkins University, Baltimore, MD, USA.|Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Pei-Hsun
authors.initials: PH
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D003116:Color; D019295:Computational Biology / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007150:Immunohistochemistry / Q000379:methods; D010338:Pathology, Clinical / Q000379:methods*; D015203:Reproducibility of Results