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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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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. |
format | Online Article Text |
id | pubmed-6412079 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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