Ontological labels for automated location of anatomical shape differences

  • Authors:

  • Shane Steinert-Threlkeld;Siamak Ardekani;Jose L. V. Mejino;Landon Todd Detwiler;James F. Brinkley;Michael Halle;Ron Kikinis;Raimond L. Winslow;Michael I. Miller;J. Tilak Ratnanather

  • Affiliations:

  • Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States;Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States;Structural Informatics Group, University of Washington, Seattle, WA, United States;Structural Informatics Group, University of Washington, Seattle, WA, United States;Structural Informatics Group, University of Washington, Seattle, WA, United States and Department of Biological Structure, University of Washington, Seattle, WA, United States;Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States;Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States;Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States;Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States and Department o ...;Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States and Department o ...

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2012

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Abstract

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.