Paper Info
Title
Automatic Spatial Plausibility Checks for Medical Object Recognition Results using a Spatio-anatomical Ontology.
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.
Year
Venue
Keywords
2010
KDIR 2010: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND INFORMATION RETRIEVAL
Medical imaging,Semantic technologies,Spatial reasoning,Formal ontologies
Field
DocType
Citations 
Spatial relation,Data mining,Ontology,Data set,Computer science,Fuzzy set,Artificial intelligence,Natural language processing,Human anatomy,Ontology (information science),Domain knowledge,Information retrieval,Cognitive neuroscience of visual object recognition
Conference
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Manuel Moller1698.77
Patrick Ernst2706.51
Andreas Dengel31926280.42
Daniel Sonntag429256.22