Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hipp...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shiva Keihaninejad, Rolf A Heckemann, Ioannis S Gousias, Joseph V Hajnal, John S Duncan, Paul Aljabar, Daniel Rueckert, Alexander Hammers
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b37a5fee449439ba36c94955580360d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8b37a5fee449439ba36c94955580360d
record_format	dspace
spelling	oai:doaj.org-article:8b37a5fee449439ba36c94955580360d2021-11-18T07:22:07ZClassification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.1932-620310.1371/journal.pone.0033096https://doaj.org/article/8b37a5fee449439ba36c94955580360d2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22523539/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study.Shiva KeihaninejadRolf A HeckemannIoannis S GousiasJoseph V HajnalJohn S DuncanPaul AljabarDaniel RueckertAlexander HammersPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 4, p e33096 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shiva Keihaninejad Rolf A Heckemann Ioannis S Gousias Joseph V Hajnal John S Duncan Paul Aljabar Daniel Rueckert Alexander Hammers Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.
description	Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study.
format	article
author	Shiva Keihaninejad Rolf A Heckemann Ioannis S Gousias Joseph V Hajnal John S Duncan Paul Aljabar Daniel Rueckert Alexander Hammers
author_facet	Shiva Keihaninejad Rolf A Heckemann Ioannis S Gousias Joseph V Hajnal John S Duncan Paul Aljabar Daniel Rueckert Alexander Hammers
author_sort	Shiva Keihaninejad
title	Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.
title_short	Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.
title_full	Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.
title_fullStr	Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.
title_full_unstemmed	Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.
title_sort	classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic mri segmentation.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/8b37a5fee449439ba36c94955580360d
work_keys_str_mv	AT shivakeihaninejad classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT rolfaheckemann classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT ioannissgousias classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT josephvhajnal classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT johnsduncan classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT paulaljabar classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT danielrueckert classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation AT alexanderhammers classificationandlateralizationoftemporallobeepilepsieswithandwithouthippocampalatrophybasedonwholebrainautomaticmrisegmentation
_version_	1718423545510363136

Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.

Ejemplares similares