Segmentation of focal cortical dysplasia lesions on MRI using level set evolution.

Focal cortical dysplasia (FCD) is the most frequent malformation of cortical development in patients with medically intractable epilepsy. On MRI, FCD lesions are not easily differentiable from the normal cortex and defining their spatial extent is challenging. In this paper, we introduce a method to segment FCD lesions on T1-weighted MRI. It relies on two successive three-dimensional deformable models, whose evolutions are based on the level set framework. The first deformable model is driven by probability maps obtained from three MRI features: cortical thickness, relative intensity and gradient. These features correspond to the visual characteristics of FCD and allow discriminating lesions and normal tissues. In a second stage, the previous result is expanded towards the underlying and overlying cortical boundaries, throughout the whole cortical section. The method was quantitatively evaluated by comparison with manually traced labels in 18 patients with FCD. The automated segmentations achieved a strong agreement with the manuals labels, demonstrating the applicability of the method to assist the delineation of FCD lesions on MRI. This new approach may become a useful tool for the presurgical evaluation of patients with intractable epilepsy related to cortical dysplasia.

Individual voxel-based analysis of gray matter in focal cortical dysplasia.

High-resolution MRI of the brain has made it possible to identify focal cortical dysplasia (FCD) in an increasing number of patients. There is evidence for structural abnormalities extending beyond the visually identified FCD lesion. Voxel-based morphometry (VBM) has the potential of detecting both lesions and extra-lesional abnormalities because it performs a whole brain voxel-wise comparison. However, on T1-weighted MRI, FCD lesions are characterized by a wide spectrum of signal hyperintensity that may compromise the results of the segmentation step in VBM. Our purpose was to investigate gray matter (GM) changes in individual FCD patients using voxel-based morphometry (VBM). In addition, we sought to assess the performance of this technique for FCD detection with respect to lesion intensity using an operator designed to emphasize areas of hyperintense T1 signal. We studied 27 patients with known FCD and focal epilepsy and 39 healthy controls. We compared the GM map of each subject (controls and patients) with the average GM map of all controls and obtained a GM z-score map for each individual. The protocol being designed to achieve a maximal specificity, no differences in GM concentration were found in the control group. The z-score maps showed an increase in GM that coincided with the lesion in 21/27 (78%) patients. Five of the six remaining patients whose lesions were not detected by VBM presented with a strong lesion hyperintensity, and a significant part of their lesion was misclassified as white matter. In 16/27 (59%) patients, there were additional areas of GM increase distant from the primary lesion. Areas of GM decrease were found in 8/27 (30%) patients. In conclusion, individual voxel-based analysis was able to detect FCD in a majority of patients. Moreover, FCD was often associated with widespread GM changes extending beyond the visible lesion. In its current form, however, individual VBM may be unable to detect lesions characterized by strong signal intensity abnormalities.

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

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.