Image metadata reasoning for improved clinical decision support

Today, clinicians rely more and more on medical images for screening, diagnosis, treatment planning, and follow-up examinations. While medical images provide a wealth of information for clinicians, content information cannot be automatically integrated into advanced medical applications such as those for the clinical decision support. The implementation of advanced medical applications requires means for the automated post-processing of medical image annotations. In this article we describe how we made use of reasoning technologies to post-process medical image annotations in the context of the automated staging process of lymphoma patients. First, we describe how automatic anatomy detectors and OWL reasoning processes can be used to preprocess medical images automatically and in a way that makes accurate input to further, more complex reasoning processes possible. Second, we enhance and integrate patients' image metadata by formalized practical clinical knowledge sources. The resulting combined data serve as input to an automatic reasoning process in order to stage lymphoma patients automatically.