Developing the Quantitative Histopathology Image Ontology (QHIO): A case study using the hot spot detection problem.

Journal: Journal of biomedical informatics
Published Date: Feb 1, 2017

Abstract

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology - QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts.

Authors

  • Metin N Gurcan
    Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA. Electronic address: metin.gurcan@osumc.edu.
  • John Tomaszewski
    Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14214, USA.
  • James A Overton
    La Jolla Institute for Allergy and Immunology, La Jolla, California, United States of America.
  • Scott Doyle
    Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14214, USA.
  • Alan Ruttenberg
    School of Dental Medicine, State University of New York at Buffalo, Buffalo, New York, United States of America.
  • Barry Smith
    Department of Philosophy, University at Buffalo, NY, USA.

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