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Ontological Labels for Automated Location of Anatomical Shape Differences

Institution:	1Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA. 2Structural Informatics Group, University of Washington, Seattle, WA, USA. 3Department of Biological Structure, University of Washington, Seattle, WA, USA. 4Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA. 5Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. 6Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:	Elsevier Science
Publication Date:	Jun-2012
Journal:	J Biomed Inform
Volume Number:	45
Issue Number:	3
Pages:	522-527
Citation:	J Biomed Inform. 2012 Jun;45(3):522-7.
PubMed ID:	22490168
PMCID:	PMC3371096
Keywords:	Ontologies, Medical atlases, Cardiac left ventricle, Computational anatomy
Appears in Collections:	NAC, SPL
Generated Citation:	Steinert-Threlkeld S., Ardekani S., Mejino J.L.V., Detwiler L.T., Brinkley J.F., Halle M., Kikinis R., Winslow R.L., Miller M.I., Ratnanather J.T. Ontological Labels for Automated Location of Anatomical Shape Differences. J Biomed Inform. 2012 Jun;45(3):522-7. PMID: 22490168. PMCID: PMC3371096.
Paper:	Download, View online
Export citation:	Bibtex Text Medline XML

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.

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