title: Automated MR image classification in temporal lobe epilepsy.
abstract: In those with drug refractory focal epilepsy, MR imaging is important for identifying structural causes of seizures that may be amenable to surgical treatment. In up to 25% of potential surgical candidates, however, MRI is reported as unremarkable even when employing epilepsy specific sequences. Automated MRI classification is a desirable tool to augment the interpretation of images, especially when changes are subtle or distributed and may be missed on visual inspection. Support vector machines (SVM) have recently been described to be useful for voxel-based MR image classification. In the present study we sought to evaluate whether this method is feasible in temporal lobe epilepsy, with adequate accuracy. We studied 38 patients with hippocampal sclerosis and unilateral (mesial) temporal lobe epilepsy (mTLE) (20 left) undergoing presurgical evaluation and 22 neurologically normal control subjects. 3D T1-weighted images were acquired at 3T (GE Excite), segmented into tissue classes, normalized and smoothed with SPM8. Diffusion tensor imaging (DTI) and double echo images for T2 relaxometry were also acquired and processed. The SVM analysis was done with the libsvm software package in a leave-one-out cross-validation design and predictive accuracy was measured. Local weighting was applied by SPM F-contrast maps. Best accuracies were achieved using the gray matter based segmentation (90-100%) and mean diffusivity (95-97%). For the three-way classification, accuracies were 88 and 93% respectively. Local weighting generally improved the accuracies except in the FA-based processing for which no effect was noted. Removing the hippocampus from the analysis, on the other hand, reduced the obtainable diagnostic indices but these were still >90% for DTI-based methods and lateralization based on gray matter maps. These findings show that automated SVM image classification can achieve high diagnostic accuracy in mTLE and that voxel-based MRI can be used at the individual subject level. This could be helpful for screening assessments of MRI scans in patients with epilepsy and when no lesion is detected on visual evaluation.
authors.affiliation: Department of Clinical Neurophysiology, Georg-August University Göttingen, Göttingen, Germany. nfocke@uni-goettingen.de
authors.forename: Niels K
authors.initials: NK
authors.lastname: Focke
authors.forename: Mahinda
authors.initials: M
authors.lastname: Yogarajah
authors.forename: Mark R
authors.initials: MR
authors.lastname: Symms
authors.forename: Oliver
authors.initials: O
authors.lastname: Gruber
authors.forename: Walter
authors.initials: W
authors.lastname: Paulus
authors.forename: John S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000328:Adult; D004833:Epilepsy, Temporal Lobe / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D060388:Support Vector Machine; D055815:Young Adult