Elsevier

Journal of Biomedical Informatics

Volume 66, February 2017, Pages 129-135
Journal of Biomedical Informatics

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

https://doi.org/10.1016/j.jbi.2016.12.006Get rights and content
Under an Elsevier user license
open archive

Highlights

  • The Quantitative Histopathological Imaging Ontology (QHIO) is proposed.

  • QHIO facilitates interoperability between histopathology datasets and algorithms.

  • By enforcing data compatibility, QHIO enables large-scale collaborative studies.

  • Researchers can easily find data and algorithms to suit their experimental needs.

  • Designed with OBO principles, QHIO integrates with existing biomedical ontologies.

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.

Keywords

Histopathology imaging
Image analysis
Hot spot
Ontology
Breast cancer

Cited by (0)