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A level set driven by MR features of focal cortical dysplasia for lesion segmentation

Summary: A level set driven by MR features of focal cortical dysplasia for
lesion segmentation
O. Colliot£, T. Mansi, N. Bernasconi, V. Naessens, D. Klironomos, A. Bernasconi
McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
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 de-
lineation 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 segmen-
tation 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.
1 Introduction
Malformations of cortical development (MCD) have been increasingly recognized as an important cause of med-
ically intractable focal epilepsy. Focal cortical dysplasia (FCD) [1], a malformation due to abnormal neuroglial
proliferation, is the most frequent MCD in patients with intractable extra-temporal epilepsy [2]. Epilepsy surgery,
consisting in the removal of the FCD lesion, is an effective treatment for these patients. However, freedom from
seizures after surgery is closely related to the resection of the whole lesion [3]. The precise delineation of lesions


Source: Ayache, Nicholas - INRIA


Collections: Computer Technologies and Information Sciences; Engineering