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Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images

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...

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Autores principales: Kulaseharan, Sancgeetha, Aminpour, Azad, Ebrahimi, Mehran, Widjaja, Elysa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412079/
https://www.ncbi.nlm.nih.gov/pubmed/30642755
http://dx.doi.org/10.1016/j.nicl.2019.101663
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author Kulaseharan, Sancgeetha
Aminpour, Azad
Ebrahimi, Mehran
Widjaja, Elysa
author_facet Kulaseharan, Sancgeetha
Aminpour, Azad
Ebrahimi, Mehran
Widjaja, Elysa
author_sort Kulaseharan, Sancgeetha
collection PubMed
description 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.
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spelling pubmed-64120792019-03-21 Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images Kulaseharan, Sancgeetha Aminpour, Azad Ebrahimi, Mehran Widjaja, Elysa Neuroimage Clin Article 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. Elsevier 2019-01-04 /pmc/articles/PMC6412079/ /pubmed/30642755 http://dx.doi.org/10.1016/j.nicl.2019.101663 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Kulaseharan, Sancgeetha
Aminpour, Azad
Ebrahimi, Mehran
Widjaja, Elysa
Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
title Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
title_full Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
title_fullStr Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
title_full_unstemmed Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
title_short Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
title_sort identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412079/
https://www.ncbi.nlm.nih.gov/pubmed/30642755
http://dx.doi.org/10.1016/j.nicl.2019.101663
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