Combining patient metadata extraction and automatic image parsing for the generation of an anatomic atlas

Authors:
Manuel Möller;Patrick Ernst;Michael Sintek;Sascha Seifert;Gunnar Grimnes;Alexander Cavallaro;Andreas Dengel
Affiliations:
German Research Center for Artificial Intelligence, Kaiserslautern, Germany and University of Kaiserslautern, Germany;University of Kaiserslautern, Germany;German Research Center for Artificial Intelligence, Kaiserslautern, Germany;Integrated Data Systems, Siemens CT, Erlangen, Germany;German Research Center for Artificial Intelligence, Kaiserslautern, Germany;University Hospital, Erlangen, Germany;German Research Center for Artificial Intelligence, Kaiserslautern, Germany and University of Kaiserslautern, Germany
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

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Abstract

We present a system that integrates ontology-based metadata extraction from medical images with a state-of-the-art object recognition algorithm for 3D volume data sets generated by Computed Tomography scanners. Extracted metadata and automatically generated medical image annotations are stored as instances of OWL classes. This system is applied to a corpus of over 750 GB of clinical image data. A spatial database is used to store and retrieve 3D representations of the generated medical image annotations. Our integrated data representation allows us to easily analyze our corpus and to estimate the quality of image metadata. A rule-based system is used to check the plausibility of the output of the automatic object recognition technique against the Foundational Model of Anatomy ontology. All combined, these methods are used to determine an appropriate set of metadata and image features for the automatic generation of a spatial atlas of human anatomy.