Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients

Autores
Guilenea, Federico Nicolás; Casciaro, Mariano Ezequiel; Pascaner, Ariel Fernando; Soulat, Gilles; Mousseaux, Elie; Craiem, Damian
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.
Fil: Guilenea, Federico Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Fil: Casciaro, Mariano Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Fil: Pascaner, Ariel Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Fil: Soulat, Gilles. Hopital Europeen Georges Pompidou; Francia
Fil: Mousseaux, Elie. Hopital Europeen Georges Pompidou; Francia
Fil: Craiem, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Materia
ARTERY CALCIUM
CONVOLUTIONAL NEURAL NETWORK
THORACIC AORTA CALCIFICATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/212659
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patientsGuilenea, Federico NicolásCasciaro, Mariano EzequielPascaner, Ariel FernandoSoulat, GillesMousseaux, ElieCraiem, DamianARTERY CALCIUMCONVOLUTIONAL NEURAL NETWORKTHORACIC AORTA CALCIFICATIONhttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.Fil: Guilenea, Federico Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Casciaro, Mariano Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Pascaner, Ariel Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Soulat, Gilles. Hopital Europeen Georges Pompidou; FranciaFil: Mousseaux, Elie. Hopital Europeen Georges Pompidou; FranciaFil: Craiem, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaMultidisciplinary Digital Publishing Institute2021-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/212659Guilenea, Federico Nicolás; Casciaro, Mariano Ezequiel; Pascaner, Ariel Fernando; Soulat, Gilles; Mousseaux, Elie; et al.; Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients; Multidisciplinary Digital Publishing Institute; Tomography; 7; 4; 12-2021; 636-6492379-139XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2379-139X/7/4/54info:eu-repo/semantics/altIdentifier/doi/10.3390/tomography7040054info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:23:22Zoai:ri.conicet.gov.ar:11336/212659instacron: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:34982025-10-15 15:23:23.048CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
title Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
spellingShingle Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
Guilenea, Federico Nicolás
ARTERY CALCIUM
CONVOLUTIONAL NEURAL NETWORK
THORACIC AORTA CALCIFICATION
title_short Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
title_full Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
title_fullStr Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
title_full_unstemmed Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
title_sort Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients
dc.creator.none.fl_str_mv Guilenea, Federico Nicolás
Casciaro, Mariano Ezequiel
Pascaner, Ariel Fernando
Soulat, Gilles
Mousseaux, Elie
Craiem, Damian
author Guilenea, Federico Nicolás
author_facet Guilenea, Federico Nicolás
Casciaro, Mariano Ezequiel
Pascaner, Ariel Fernando
Soulat, Gilles
Mousseaux, Elie
Craiem, Damian
author_role author
author2 Casciaro, Mariano Ezequiel
Pascaner, Ariel Fernando
Soulat, Gilles
Mousseaux, Elie
Craiem, Damian
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv ARTERY CALCIUM
CONVOLUTIONAL NEURAL NETWORK
THORACIC AORTA CALCIFICATION
topic ARTERY CALCIUM
CONVOLUTIONAL NEURAL NETWORK
THORACIC AORTA CALCIFICATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.
Fil: Guilenea, Federico Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Fil: Casciaro, Mariano Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Fil: Pascaner, Ariel Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
Fil: Soulat, Gilles. Hopital Europeen Georges Pompidou; Francia
Fil: Mousseaux, Elie. Hopital Europeen Georges Pompidou; Francia
Fil: Craiem, Damian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentina
description Arterial calcification is an independent predictor of cardiovascular disease (CVD) events whereas thoracic aorta calcium (TAC) detection might anticipate extracoronary outcomes. In this work, we trained six convolutional neural networks (CNNs) to detect aortic calcifications and to automate the TAC score assessment in intermediate CVD risk patients. Cardiac computed tomography images from 1415 patients were analyzed together with their aortic geometry previously assessed. Orthogonal patches centered in each aortic candidate lesion were reconstructed and a dataset with 19,790 images (61% positives) was built. Three single-input 2D CNNs were trained using axial, coronal and sagittal patches together with two multi-input 2.5D CNNs combining the orthogonal patches and identifying their best regional combination (BRC) in terms of lesion location. Aortic calcifications were concentrated in the descending (66%) and aortic arch (26%) portions. The BRC of axial patches to detect ascending or aortic arch lesions and sagittal images for the descending portion had the best performance: 0.954 F1-Score, 98.4% sensitivity, 87% of the subjects correctly classified in their TAC category and an average false positive TAC score per patient of 30. A CNN that combined axial and sagittal patches depending on the candidate aortic location ensured an accurate TAC score prediction.
publishDate 2021
dc.date.none.fl_str_mv 2021-12
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/11336/212659
Guilenea, Federico Nicolás; Casciaro, Mariano Ezequiel; Pascaner, Ariel Fernando; Soulat, Gilles; Mousseaux, Elie; et al.; Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients; Multidisciplinary Digital Publishing Institute; Tomography; 7; 4; 12-2021; 636-649
2379-139X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/212659
identifier_str_mv Guilenea, Federico Nicolás; Casciaro, Mariano Ezequiel; Pascaner, Ariel Fernando; Soulat, Gilles; Mousseaux, Elie; et al.; Thoracic aorta calcium detection and quantification using convolutional neural networks in a large cohort of intermediate-risk patients; Multidisciplinary Digital Publishing Institute; Tomography; 7; 4; 12-2021; 636-649
2379-139X
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.mdpi.com/2379-139X/7/4/54
info:eu-repo/semantics/altIdentifier/doi/10.3390/tomography7040054
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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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