A hybrid system for the semantic annotation of sulco-gyral anatomy in MRI images.
This paper presents an interactive system for the annotation of brain anatomical structures in Magnetic Resonance Images. The system is based on hybrid knowledge and techniques. First, it exploits both numerical knowledge from atlases and symbolic knowledge from a rule-extended ontology represented in OWL, the Web ontology language, and combines them with graphical data about cortical sulci, automatically extracted from the images. Second, the annotations of the parts of gyri and of sulci located in a region of interest are obtained with different reasoning techniques: Constraint Satisfaction Solving and Description Logics techniques. Preliminary experiments have been achieved on normal and also pathological data. The results obtained so far are very promising.
Mechouche
Ammar
A
Unit/Project VisAGeS U746, INSERM-INRIA-CNRS-Univ-Rennes 1, France. Ammar.Mechouche@irisa.fr
Morandi
Xavier
X
Golbreich
Christine
C
Gibaud
Bernard
B
D000465:Algorithms; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003198:Computer Simulation; D004282:Documentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012660:Semantics; D012680:Sensitivity and Specificity