Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT1...

Autores
Chaves, Hernan; Dorr, Francisco; Costa, Martín Elías; Serra, María Mercedes; Fernandez Slezak, Diego; Farez, Mauricio Franco; Sevlever, Gustavo; Yañez, Paulina Celia; Cejas, Claudia
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and Methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94−0.97)) than FreeSurfer and CAT12 (0.92 (0.88−0.96)) and FSL (0.87 (0.79−0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20–3.13% vs. mean CV 1.05, range 0.21–3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49–5.91% vs. mean CV 3.84, range 2.62–5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.
Fil: Chaves, Hernan. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Dorr, Francisco. Entelai; Argentina
Fil: Costa, Martín Elías. Entelai; Argentina
Fil: Serra, María Mercedes. Entelai; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Fernandez Slezak, Diego. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Entelai; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Farez, Mauricio Franco. Entelai; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sevlever, Gustavo. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Entelai; Argentina
Fil: Yañez, Paulina Celia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Universidad de Buenos Aires; Argentina
Fil: Cejas, Claudia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Materia
BRAIN
DEEP LEARNING
FREESURFER.
MAGNETIC RESONANCE IMAGING
SEGMENTATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/182236
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/182236
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSLChaves, HernanDorr, FranciscoCosta, Martín ElíasSerra, María MercedesFernandez Slezak, DiegoFarez, Mauricio FrancoSevlever, GustavoYañez, Paulina CeliaCejas, ClaudiaBRAINDEEP LEARNINGFREESURFER.MAGNETIC RESONANCE IMAGINGSEGMENTATIONhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and Methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94−0.97)) than FreeSurfer and CAT12 (0.92 (0.88−0.96)) and FSL (0.87 (0.79−0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20–3.13% vs. mean CV 1.05, range 0.21–3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49–5.91% vs. mean CV 3.84, range 2.62–5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.Fil: Chaves, Hernan. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Dorr, Francisco. Entelai; ArgentinaFil: Costa, Martín Elías. Entelai; ArgentinaFil: Serra, María Mercedes. Entelai; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Fernandez Slezak, Diego. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Entelai; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Farez, Mauricio Franco. Entelai; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sevlever, Gustavo. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Entelai; ArgentinaFil: Yañez, Paulina Celia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Universidad de Buenos Aires; ArgentinaFil: Cejas, Claudia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaElsevier2021-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/182236Chaves, Hernan; Dorr, Francisco; Costa, Martín Elías; Serra, María Mercedes; Fernandez Slezak, Diego; et al.; Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL; Elsevier; Journal Of Neuroradiology. Journal de Neuroradiologie.; 48; 3; 5-2021; 147-1560150-98611773-0406CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0150986120302807info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neurad.2020.10.001info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:05:31Zoai:ri.conicet.gov.ar:11336/182236instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:05:31.804CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
title Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
spellingShingle Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
Chaves, Hernan
BRAIN
DEEP LEARNING
FREESURFER.
MAGNETIC RESONANCE IMAGING
SEGMENTATION
title_short Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
title_full Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
title_fullStr Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
title_full_unstemmed Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
title_sort Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
dc.creator.none.fl_str_mv Chaves, Hernan
Dorr, Francisco
Costa, Martín Elías
Serra, María Mercedes
Fernandez Slezak, Diego
Farez, Mauricio Franco
Sevlever, Gustavo
Yañez, Paulina Celia
Cejas, Claudia
author Chaves, Hernan
author_facet Chaves, Hernan
Dorr, Francisco
Costa, Martín Elías
Serra, María Mercedes
Fernandez Slezak, Diego
Farez, Mauricio Franco
Sevlever, Gustavo
Yañez, Paulina Celia
Cejas, Claudia
author_role author
author2 Dorr, Francisco
Costa, Martín Elías
Serra, María Mercedes
Fernandez Slezak, Diego
Farez, Mauricio Franco
Sevlever, Gustavo
Yañez, Paulina Celia
Cejas, Claudia
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv BRAIN
DEEP LEARNING
FREESURFER.
MAGNETIC RESONANCE IMAGING
SEGMENTATION
topic BRAIN
DEEP LEARNING
FREESURFER.
MAGNETIC RESONANCE IMAGING
SEGMENTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and Methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94−0.97)) than FreeSurfer and CAT12 (0.92 (0.88−0.96)) and FSL (0.87 (0.79−0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20–3.13% vs. mean CV 1.05, range 0.21–3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49–5.91% vs. mean CV 3.84, range 2.62–5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.
Fil: Chaves, Hernan. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Dorr, Francisco. Entelai; Argentina
Fil: Costa, Martín Elías. Entelai; Argentina
Fil: Serra, María Mercedes. Entelai; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
Fil: Fernandez Slezak, Diego. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Entelai; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Farez, Mauricio Franco. Entelai; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Sevlever, Gustavo. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Entelai; Argentina
Fil: Yañez, Paulina Celia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina. Universidad de Buenos Aires; Argentina
Fil: Cejas, Claudia. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; Argentina
description Background and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and Methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94−0.97)) than FreeSurfer and CAT12 (0.92 (0.88−0.96)) and FSL (0.87 (0.79−0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20–3.13% vs. mean CV 1.05, range 0.21–3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49–5.91% vs. mean CV 3.84, range 2.62–5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.
publishDate 2021
dc.date.none.fl_str_mv 2021-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/182236
Chaves, Hernan; Dorr, Francisco; Costa, Martín Elías; Serra, María Mercedes; Fernandez Slezak, Diego; et al.; Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL; Elsevier; Journal Of Neuroradiology. Journal de Neuroradiologie.; 48; 3; 5-2021; 147-156
0150-9861
1773-0406
CONICET Digital
CONICET
url http://hdl.handle.net/11336/182236
identifier_str_mv Chaves, Hernan; Dorr, Francisco; Costa, Martín Elías; Serra, María Mercedes; Fernandez Slezak, Diego; et al.; Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL; Elsevier; Journal Of Neuroradiology. Journal de Neuroradiologie.; 48; 3; 5-2021; 147-156
0150-9861
1773-0406
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0150986120302807
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neurad.2020.10.001
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.885934

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