Segmentation of focal cortical dysplasia lesions using a feature-based level set

Authors:
O. Colliot;T. Mansi;N. Bernasconi;V. Naessens;D. Klironomos;A. Bernasconi
Affiliations:
Montreal Neurological Institute, McGill University, Montreal, Canada;Montreal Neurological Institute, McGill University, Montreal, Canada;Montreal Neurological Institute, McGill University, Montreal, Canada;Montreal Neurological Institute, McGill University, Montreal, Canada;Montreal Neurological Institute, McGill University, Montreal, Canada;Montreal Neurological Institute, McGill University, Montreal, Canada
Venue:
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2005

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Abstract

Focal cortical dysplasia (FCD), a malformation of cortical development, is an important cause of medically intractable epilepsy. FCD lesions are difficult to distinguish from non-lesional cortex and their delineation on MRI is a challenging task. This paper presents a method to segment FCD lesions on T1-weighted MRI, based on a 3D deformable model, implemented using the level set framework. The deformable model is driven by three MRI features: cortical thickness, relative intensity and gradient. These features correspond to the visual characteristics of FCD and allow to differentiate lesions from normal tissues. The proposed method was tested on 18 patients with FCD and its performance was quantitatively evaluated by comparison with the manual tracings of two trained raters. The validation showed that the similarity between the level set segmentation and the manual labels is similar to the agreement between the two human raters. This new approach may become a useful tool for the presurgical evaluation of patients with intractable epilepsy.