TY - JOUR
T1 - Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
AU - Kulaseharan, Sancgeetha
AU - Aminpour, Azad
AU - Ebrahimi, Mehran
AU - Widjaja, Elysa
N1 - Funding Information:
This research was conducted with the support of EpLink – The Epilepsy Research Program of the Ontario Brain Institute (OBI). The OBI is an independent non-profit corporation, funded partially by the Ontario government. The opinions, results and conclusions are those of the authors and no endorsement by the Ontario Brain Institute is intended or should be inferred. This research was also supported in part by an NSERC Discovery Grant for M.E.
Publisher Copyright:
© 2019 The Authors
PY - 2019
Y1 - 2019
N2 - We seek to examine the use of an image processing pipeline on Magnetic Resonance Imaging (MRI) to identify features of Focal Cortical Dysplasia (FCD) in children who were suspected to have FCD on MRI (MRI-positive) and those with MRI-negative epilepsy. We aim to use a computer-aided diagnosis system to identify epileptogenic lesions with a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences. We implemented a modified version of the 2-Step Bayesian classifier method to a paediatric cohort with medically intractable epilepsy with MRI-positive and MRI-negative epilepsy, and evaluated the performance of the algorithm trained on textural features derived from T1-weighted (T1-w), T2-weighted (T2-w), and FLAIR (Fluid Attenuated Inversion Recovery) sequences. For MRI-positive cases, T1-w has the highest subjectwise sensitivity relative to T2-w and FLAIR (94% vs. 90% vs. 71% respectively), and also the highest lesional sensitivity (63% vs. 60% vs. 42% respectively), but the lowest lesional specificity (75% vs. 80% vs. 89% respectively). Combination of all three sequences improved the performance of the algorithm, with 97% subjectwise sensitivity. For MRI-negative cases, T1-w has the highest subjectwise sensitivity relative to T2-w and FLAIR (48% vs. 30% vs. 39% respectively), and also the highest lesional sensitivity (31% vs. 22% vs. 28% respectively). However, T2-w has the highest lesional specificity relative to T1-w and FLAIR (95% vs. 94% vs. 92% respectively) for MRI-negative cases. Combination of all three sequences improved the performance of the algorithm, with 70% subjectwise sensitivity. The 2-Step Naïve Bayes classifier correctly rejected 100% of the healthy subjects for all three sequences. Using combined morphometric and textural analysis in a 2-Step Bayesian classifier, applied to multiple MRI sequences, can assist with lesion detection in children with intractable epilepsy.
AB - We seek to examine the use of an image processing pipeline on Magnetic Resonance Imaging (MRI) to identify features of Focal Cortical Dysplasia (FCD) in children who were suspected to have FCD on MRI (MRI-positive) and those with MRI-negative epilepsy. We aim to use a computer-aided diagnosis system to identify epileptogenic lesions with a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences. We implemented a modified version of the 2-Step Bayesian classifier method to a paediatric cohort with medically intractable epilepsy with MRI-positive and MRI-negative epilepsy, and evaluated the performance of the algorithm trained on textural features derived from T1-weighted (T1-w), T2-weighted (T2-w), and FLAIR (Fluid Attenuated Inversion Recovery) sequences. For MRI-positive cases, T1-w has the highest subjectwise sensitivity relative to T2-w and FLAIR (94% vs. 90% vs. 71% respectively), and also the highest lesional sensitivity (63% vs. 60% vs. 42% respectively), but the lowest lesional specificity (75% vs. 80% vs. 89% respectively). Combination of all three sequences improved the performance of the algorithm, with 97% subjectwise sensitivity. For MRI-negative cases, T1-w has the highest subjectwise sensitivity relative to T2-w and FLAIR (48% vs. 30% vs. 39% respectively), and also the highest lesional sensitivity (31% vs. 22% vs. 28% respectively). However, T2-w has the highest lesional specificity relative to T1-w and FLAIR (95% vs. 94% vs. 92% respectively) for MRI-negative cases. Combination of all three sequences improved the performance of the algorithm, with 70% subjectwise sensitivity. The 2-Step Naïve Bayes classifier correctly rejected 100% of the healthy subjects for all three sequences. Using combined morphometric and textural analysis in a 2-Step Bayesian classifier, applied to multiple MRI sequences, can assist with lesion detection in children with intractable epilepsy.
KW - Computer-aided diagnosis
KW - Epilepsy
KW - Focal cortical dysplasia
KW - Morphometric and textural analysis
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U2 - 10.1016/j.nicl.2019.101663
DO - 10.1016/j.nicl.2019.101663
M3 - Article
C2 - 30642755
AN - SCOPUS:85059776476
SN - 2213-1582
VL - 21
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
M1 - 101663
ER -