Automatic Spatial Plausibility Checks for Medical Object Recognition Results using a Spatio-anatomical Ontology.

We present an approach to use medical expert knowledge represented in formal ontologies to check the results of automatic medical object recognition algorithms for spatial plausibility. Our system is based on the comprehensive Foundation Model of Anatomy ontology which we extend with spatial relations between a number of anatomical entities. These relations are learned inductively from an annotated corpus of 3D volume data sets. The induction process is split into two parts: First, we generate a quantitative anatomical atlas using fuzzy sets to represent inherent imprecision. From this atlas we abstract onto a purely symbolic level to generate a generic qualitative model of the spatial relations in human anatomy. In our evaluation we describe how this model can be used to check the results of a state-of-the-art medical object recognition system for 3D CT volume data sets for spatial plausibility. Our results show that the combination of medical domain knowledge in formal ontologies and sub-symbolic object recognition yields improved overall recognition precision.