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Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations

Manuel Möller¹, Christian Folz², Michael Sintek¹, Sascha Seifert³ & Pinar Wennerberg⁴

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1. German Research Center for Artificial Intelligence
2. University of Applied Sciences Kaiserslautern, Germany
3. Siemens AG, Corporate Technology, Erlangen, Germany
4. Siemens AG, Corporate Technology, Munich, Germany

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Document Type:

Manuscript

Date:

Received 27 July 2009 16:01 UTC; Posted 27 July 2009

Subjects:

Bioinformatics

Tags:

• formal ontology
• spatial processing
• human anatomy
• database
• model

Abstract:

Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently available in the FMA. In this publication we present a technique for the automatic inductive acquisition of spatial relation instances by generalizing from expert-annotated volume datasets.

Collection:

International Conference on Biomedical Ontology

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This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Möller, Manuel, Folz, Christian , Sintek, Michael, Seifert, Sascha, and Wennerberg, Pinar. Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2009.3471.1> (2009)

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