Highlights
- •Gray–white matter lesions are detected by combining GPML with multimodal features.
- •Classifier modeled using DTI indexes can increase the sensitivity of detection.
- •GPML is robust to unbalanced datasets and performs better than conventional SVM.
- •Accurate detection may contribute to successful surgery and lower surgical risk.
Abstract
The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions
in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging
(DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated
classifier using voxel-based multimodal features to identify gray and white matter
abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted
image intensity feature, DTI-based features were also utilized. A Gaussian processes
for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with
histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out
strategy. Moreover, we compared the multimodal GPML paradigm's performance with that
of single modal GPML and classical support vector machine (SVM). Our results demonstrated
that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the
GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly,
GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based
features to multimodal paradigm. Based on the results, it can also be clearly stated
that the proposed GPML strategy performed better and is robust to unbalanced dataset
contrary to SVM that performed poorly (AUC = 0.69). Therefore, the GPML paradigm using
multimodal MRI data containing DTI modality has promising result towards detection
of the FCD lesions and provides an effective direction for future researches.
Keywords
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Article info
Publication history
Published online: May 21, 2018
Accepted:
April 13,
2018
Received in revised form:
March 31,
2018
Received:
February 5,
2018
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
