Biomedical imaging ontologies: A survey and proposal for future work

B Smith (1) , S Arabandi (2) , M Brochhausen (3) , M Calhoun (4) , P Ciccarese (5) , S Doyle (6) , B Gibaud (7) , I Goldberg (8) , Jr Kahn C (9) , J Overton (10) , J Tomaszewski (6) , M. Gurcan (11)

1 Department of Philosophy
2 Ontopro LLC
3 Division of Biomedical Informatics
4 Department of Health and Human Performance
5 HMS - Harvard Medical School [Boston]
6 Department of Pathology and Anatomical Sciences
7 LTSI - Laboratoire Traitement du Signal et de l'Image
8 National Institute on Aging, National Institutes of Health
9 Department of Radiology
10 Knocean
11 Department of Biomedical Informatics [Columbus]

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 definitions 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.

Keywords: ontology; quantitative histopathology image ontology; Histopathology imaging; interoperability

Domains: Life Sciences [q-bio]; Bioengineering

B Smith, S Arabandi, M Brochhausen, M Calhoun, P Ciccarese, et al.. Biomedical imaging ontologies: A survey and proposal for future work. Journal of Pathology Informatics, 2015, 6 (1), pp.37. ⟨10.4103/2153-3539.159214⟩. ⟨hal-01212447⟩