title: Automatic recognition of cortical sulci of the human brain using a congregation of neural networks.
abstract: This paper describes a complete system allowing automatic recognition of the main sulci of the human cortex. This system relies on a preprocessing of magnetic resonance images leading to abstract structural representations of the cortical folding patterns. The representation nodes are cortical folds, which are given a sulcus name by a contextual pattern recognition method. This method can be interpreted as a graph matching approach, which is driven by the minimization of a global function made up of local potentials. Each potential is a measure of the likelihood of the labelling of a restricted area. This potential is given by a multi-layer perceptron trained on a learning database. A base of 26 brains manually labelled by a neuroanatomist is used to validate our approach. The whole system developed for the right hemisphere is made up of 265 neural networks. The mean recognition rate is 86% for the learning base and 76% for a generalization base, which is very satisfying considering the current weak understanding of the variability of the cortical folding patterns.
authors.affiliation: Service Hospitalier Frédéric Joliot, CEA, 4 place du Général Leclerc, 91401 Orsay, France. riviere@shfj.cea.fr
authors.forename: Denis
authors.initials: D
authors.lastname: Rivière
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Mangin
authors.forename: Dimitri
authors.initials: D
authors.lastname: Papadopoulos-Orfanos
authors.forename: Jean-Marc
authors.initials: JM
authors.lastname: Martinez
authors.forename: Vincent
authors.initials: V
authors.lastname: Frouin
authors.forename: Jean
authors.initials: J
authors.lastname: Régis
mesh_terms: D001185:Artificial Intelligence; D001331:Automation; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007858:Learning; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D013598:Systems Theory