Abstract
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.
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