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.
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Taylor, D.C., Falconer, M.A., Bruton, C.J., Corsellis, J.A.N.: Focal dysplasia of the cerebral cortex in epilepsy. J. Neurol Neurosurg Psychiatry 34, 369–387 (1971)
Sisodiya, S.: Surgery for malformations of cortical development causing epilepsy. Brain 123, 1075–1091 (2000)
Tassi, L., Colombo, N., et al.: Focal cortical dysplasia: neuropathological subtypes, EEG, neuroimaging and surgical outcome. Brain 125, 1719–1732 (2002)
Antel, S., Collins, D., et al.: Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis. NeuroImage 19, 1748–1759 (2003)
Wilke, M., Kassubek, J., Ziyeh, S., Schulze-Bonhage, A., Huppertz, H.: Automated detection of gray matter malformations using optimized voxel-based morphometry: a systematic approach. NeuroImage 20, 330–343 (2003)
Antel, S., Bernasconi, A., et al.: Computational models of MRI characteristics of focal cortical dysplasia improve lesion detection. NeuroImage 17, 1755–1760 (2002)
Barkovich, A., Kuzniecky, R.: Neuroimaging of focal malformations of cortical development. J. Clin. Neurophysiol 13, 481–494 (1996)
Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/MDL for multiband image segmentation. IEEE TPAMI 18, 884–900 (1996)
Osher, S., Sethian, J.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulation. J. Comp. Phys. 79, 12–49 (1988)
Sethian, J.: Level-set methods and fast marching methods, 2nd edn. Cambridge University Press, Cambridge (1999)
Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. Int. J. Comp. Vis. 46, 223–247 (2002)
Adalsteinsson, D., Sethian, J.: A fast level set method for propagating interfaces. J. Comp. Phys., 269–277 (1995)
Krissian, K., Westin, C.F.: Fast sub-voxel re-initialization of the distance map for level set methods. Patt. Recog. Letters (2005) (in press)
Sled, J., Zijdenbos, A., Evans, A.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE TMI 17, 87–97 (1998)
Collins, D., et al.: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 18, 192–205 (1994)
Smith, S.: Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002)
Zijdenbos, A., Dawant, B., et al.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE TMI 13, 716–724 (1994)
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Colliot, O., Mansi, T., Bernasconi, N., Naessens, V., Klironomos, D., Bernasconi, A. (2005). Segmentation of Focal Cortical Dysplasia Lesions Using a Feature-Based Level Set. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_47
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DOI: https://doi.org/10.1007/11566465_47
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