Journal of Pathology Informatics Journal of Pathology Informatics
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Year : 2015  |  Volume : 6  |  Issue : 1  |  Page : 37

Biomedical imaging ontologies: A survey and proposal for future work

1 Department of Philosophy, The State University of New York at Buffalo, Buffalo, NY 14260, USA
2 Ontopro LLC, Houston, TX 77025, USA
3 Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
4 Department of Health and Human Performance, Elon University, Elon, NC 27244, USA
5 Harvard Medical School, Massachusetts General Hospital, PerkinElmer Innovation Labs, Boston, MA 02115, USA
6 Department of Pathology and Anatomical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA
7 Laboratoire du Traitement du Signal et de l'Image (LTSI), Inserm Unit 1099, University of Rennes 1, Rennes, France
8 National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
9 Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
10 Knocean, Toronto, Ontario, M6P 2T3, Canada
11 Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA

Correspondence Address:
Dr. Metin Gurcan
Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2153-3539.159214

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Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as "cell" or "image" or "tissue" or "microscope") that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data.

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