A hybrid system using symbolic and numeric knowledge for the semantic annotation of sulco-gyral anatomy in brain MRI images.

Ammar Mechouche; Xavier Morandi; Christine Golbreich; Bernard Gibaud

A hybrid system using symbolic and numeric knowledge for the semantic annotation of sulco-gyral anatomy in brain MRI images.

Ammar Mechouche ^{1, *} Xavier Morandi ^{1, 2} Christine Golbreich ³ Bernard Gibaud ¹

* Corresponding author

1 VisAGeS - Vision, Action et Gestion d'informations en Santé

INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE

2 Service de neurochirurgie [Rennes]

3 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier

Abstract : This paper describes an interactive system for the semantic annotation of brain magnetic resonance images. The system uses both a numerical atlas and symbolic knowledge of brain anatomical structures depicted using the Semantic Web standards. This knowledge is combined with graphical data, automatically extracted from the images by imaging tools. The annotations of parts of gyri and sulci, in a region of interest, rely on constraint satisfaction problem solving and description logics inferences. The system is run on a client-server architecture, using Web services and including a sophisticated visualization tool. An evaluation of the system was done using normal (healthy) and pathological cases. The results obtained so far demonstrate that the system produces annotations with high precision and quality.

Keywords : Reasoning Semantic Annotation Brain Anatomy MR Images Biomedical ontologies Rules Semantic Web Technologies Reasoning.

Document type :

Journal articles

Domain :

Life Sciences [q-bio] / Bioengineering

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https://www.hal.inserm.fr/inserm-00411401
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Submitted on : Thursday, August 27, 2009 - 12:06:28 PM
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A hybrid system using symbolic and numeric knowledge for the semantic annotation of sulco-gyral anatomy in brain MRI images.

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A hybrid system using symbolic and numeric knowledge for the semantic annotation of sulco-gyral anatomy in brain MRI images.

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