title: Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons.
abstract: Epilepsy is a disorder of the brain that can lead to acute crisis and temporary loss of brain functions. Surgery is used to remove focal lesions that remain resistant to treatment. An accurate localization of epileptogenic lesions has a strong influence on the outcome of epilepsy surgery. Magnetic resonance imaging (MRI) is clinically used for lesion detection and treatment planning, mainly through simple visual analysis. However, visual inspection in MRI can be highly subjective and subtle 3D structural abnormalities are not always entirely removed during surgery. In this paper, we introduce a lesion abnormality score based on computerized comparison of the 3D texture properties between brain hemispheres in T1 MRI. Overlapping cubic texture blocks extracted from user-defined 3D regions of interest (ROI) are expressed in terms of energies of 3D steerable Riesz wavelets. The abnormality score is defined as the Hausdorff distance between the ROI and its corresponding contralateral region in the brain, both expressed as ensembles of blocks in the feature space. A classification based on the proposed score allowed an accuracy of 85% with 10 control subjects and 8 patients with epileptogenic lesions. The approach therefore constitutes a valuable tool for the objective pre-surgical evaluation of patients undergoing epilepsy surgery.
authors.affiliation: University of Applied Sciences Western Switzerland (HES-SO).
authors.forename: Oscar Alfonso
authors.initials: OA
authors.lastname: Jiménez del Toro
authors.affiliation: University of Applied Sciences Western Switzerland (HES-SO).
authors.forename: Antonio
authors.initials: A
authors.lastname: Foncubierta-Rodríguez
authors.affiliation: University and University Hospitals of Geneva, Switzerland.
authors.forename: Maria Isabel
authors.initials: MI
authors.lastname: Vargas Gómez
authors.affiliation: University of Applied Sciences Western Switzerland (HES-SO).
authors.forename: Henning
authors.initials: H
authors.lastname: Müller
authors.affiliation: University of Applied Sciences Western Switzerland (HES-SO).
authors.forename: Adrien
authors.initials: A
authors.lastname: Depeursinge
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D004827:Epilepsy / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009415:Nerve Net / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity