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Biomedical imaging ontologies: A survey and proposal for future work.

Barry Smith1, Sivaram Arabandi2, Mathias Brochhausen3

  • 1Department of Philosophy, The State University of New York at Buffalo, Buffalo, NY 14260, USA.

Journal of Pathology Informatics
|July 14, 2015
PubMed
Summary

This study surveys biomedical imaging ontologies, highlighting challenges in histopathology and image analysis. It proposes a strategy for quantitative histopathology imaging to improve data integration for disease understanding.

Keywords:
Histopathology imaginginteroperabilityontologyquantitative histopathology image ontology

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Area of Science:

  • Biomedical Imaging
  • Ontology Development
  • Data Interoperability

Background:

  • Ontologies provide controlled vocabularies for consistent data tagging, promoting interoperability of heterogeneous biomedical data.
  • Key terms like "cell," "image," "tissue," and "microscope" form the basis of ontologies, with logical definitions enabling data reasoning.
  • Ontologies are crucial for standardizing terminology in scientific domains.

Purpose of the Study:

  • To survey existing biomedical imaging ontologies.
  • To identify challenges in histopathological imaging and image analysis ontology development.
  • To propose a strategy for quantitative histopathology imaging.

Main Methods:

  • Literature review of biomedical imaging ontologies.
  • Analysis of challenges in histopathology and image analysis domains.
  • Development of a strategic approach for quantitative histopathology imaging.

Main Results:

  • A comprehensive survey of current biomedical imaging ontologies.
  • Identification of specific challenges in applying ontologies to histopathological imaging.
  • A proposed strategy tailored for quantitative histopathology imaging.

Conclusions:

  • Integrating imaging data with clinical and genomics data is essential for multiscale disease understanding.
  • Interoperable ontologies are key to achieving this integration.
  • The proposed strategy aims to facilitate the use of quantitative histopathology imaging data.