Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net

Magnetic resonance angiography (MRA) is an important tool for aortic assessment in several cardiovascular diseases. Assessment of MRA images relies on manual segmentation, a time-intensive process that is subject to operator variability. We aimed to optimize and validate two deep-learning models for...

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Autores: Cesario, Matteo, Littlewood, Simon J., Nadel, James, Fletcher, Thomas J., Fotaki, Anastasia, Castillo-Passi, Carlos, Hajhosseiny, Reza, Pouliopoulos, Jim, Jabbour, Andrew, Olivero, Ruperto, Rodríguez Palomares, José Fernando|||0000-0002-8822-5532, Kooi, M. Eline, Prieto, Claudia, Botnar, René M.
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:326295
Acceso en línea:https://ddd.uab.cat/record/326295
https://dx.doi.org/urn:doi:10.1016/j.jocmr.2025.101923
Access Level:acceso abierto
Palabra clave:Aorta
Aortic disease
Magnetic resonance angiography
Segmentation
Deep-learning
nnUNet
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spelling Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-NetCesario, MatteoLittlewood, Simon J.Nadel, JamesFletcher, Thomas J.Fotaki, AnastasiaCastillo-Passi, CarlosHajhosseiny, RezaPouliopoulos, JimJabbour, AndrewOlivero, RupertoRodríguez Palomares, José Fernando|||0000-0002-8822-5532Kooi, M. ElinePrieto, ClaudiaBotnar, René M.AortaAortic diseaseMagnetic resonance angiographySegmentationDeep-learningnnUNetMagnetic resonance angiography (MRA) is an important tool for aortic assessment in several cardiovascular diseases. Assessment of MRA images relies on manual segmentation, a time-intensive process that is subject to operator variability. We aimed to optimize and validate two deep-learning models for automatic segmentation of the aortic lumen and vessel wall in high-resolution electrocardiogram-triggered free-breathing respiratory motion-corrected three-dimensional (3D) bright- and black-blood MRA images. Manual segmentation, serving as the ground truth, was performed on 25 bright-blood and 15 black-blood 3D MRA image sets acquired with the iT2PrepIR-BOOST sequence (1.5T) in thoracic aortopathy patients. The training was performed with no new U-Net (nnUNet) for bright-blood (lumen) and black-blood image sets (lumen and vessel wall). Training consisted of a 70:20:10% (17/25:5/25:3/25 datasets) training:validation:testing split. Inference was run on datasets (single vendor) from different centers (UK, Spain, and Australia), sequences (iT2PrepIR-BOOST, T2 prepared coronary magnetic resonance angiography [CMRA], and time-resolved angiography with interleaved stochastic trajectories [TWIST] MRA), acquired resolutions (from 0.9-3 mm 3), and field strengths (0.55T, 1.5T, and 3T). Predictive measurements comprised Dice similarity coefficient (DSC) and Intersection over Union (IoU). Postprocessing (3D slicer) included centreline extraction, diameter measurement, and curved planar reformatting (CPR). The optimal configuration was the 3D U-Net. Bright-blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm 3) and 3D CMRA datasets (0.9 mm 3) resulted in DSC ≥ 0.96 and IoU ≥ 0.92. For bright-blood segmentation on 3D CMRA at 0.55T, the nnUNet achieved DSC and IoU scores of 0.93 and 0.88 at 1.5 mm³, and 0.68 and 0.52 at 3.0 mm³, respectively. DSC and IoU scores of 0.89 and 0.82 were obtained for CMRA image sets (1 mm 3) at 1.5T (Barcelona dataset). DSC and IoU scores of the BRnnUNet model were 0.90 and 0.82, respectively, for the contrast-enhanced dataset (TWIST MRA). Lumen segmentation on black-blood 1.5T iT2PrepIR-BOOST image sets achieved DSC ≥ 0.95 and IoU ≥ 0.90, and vessel wall segmentation resulted in DSC ≥ 0.80 and IoU ≥ 0.67. Automated centreline tracking, diameter measurement, and CPR were successfully implemented in all subjects. Automated aortic lumen and wall segmentation on 3D bright- and black-blood image sets demonstrated excellent agreement with ground truth. This technique demonstrates a fast and comprehensive assessment of aortic morphology with great potential for future clinical application in various cardiovascular diseases. 22025-01-0120252025-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/326295https://dx.doi.org/urn:doi:10.1016/j.jocmr.2025.101923reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3262952026-06-06T12:50:31Z
dc.title.none.fl_str_mv Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
title Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
spellingShingle Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
Cesario, Matteo
Aorta
Aortic disease
Magnetic resonance angiography
Segmentation
Deep-learning
nnUNet
title_short Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
title_full Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
title_fullStr Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
title_full_unstemmed Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
title_sort Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
dc.creator.none.fl_str_mv Cesario, Matteo
Littlewood, Simon J.
Nadel, James
Fletcher, Thomas J.
Fotaki, Anastasia
Castillo-Passi, Carlos
Hajhosseiny, Reza
Pouliopoulos, Jim
Jabbour, Andrew
Olivero, Ruperto
Rodríguez Palomares, José Fernando|||0000-0002-8822-5532
Kooi, M. Eline
Prieto, Claudia
Botnar, René M.
author Cesario, Matteo
author_facet Cesario, Matteo
Littlewood, Simon J.
Nadel, James
Fletcher, Thomas J.
Fotaki, Anastasia
Castillo-Passi, Carlos
Hajhosseiny, Reza
Pouliopoulos, Jim
Jabbour, Andrew
Olivero, Ruperto
Rodríguez Palomares, José Fernando|||0000-0002-8822-5532
Kooi, M. Eline
Prieto, Claudia
Botnar, René M.
author_role author
author2 Littlewood, Simon J.
Nadel, James
Fletcher, Thomas J.
Fotaki, Anastasia
Castillo-Passi, Carlos
Hajhosseiny, Reza
Pouliopoulos, Jim
Jabbour, Andrew
Olivero, Ruperto
Rodríguez Palomares, José Fernando|||0000-0002-8822-5532
Kooi, M. Eline
Prieto, Claudia
Botnar, René M.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Aorta
Aortic disease
Magnetic resonance angiography
Segmentation
Deep-learning
nnUNet
topic Aorta
Aortic disease
Magnetic resonance angiography
Segmentation
Deep-learning
nnUNet
description Magnetic resonance angiography (MRA) is an important tool for aortic assessment in several cardiovascular diseases. Assessment of MRA images relies on manual segmentation, a time-intensive process that is subject to operator variability. We aimed to optimize and validate two deep-learning models for automatic segmentation of the aortic lumen and vessel wall in high-resolution electrocardiogram-triggered free-breathing respiratory motion-corrected three-dimensional (3D) bright- and black-blood MRA images. Manual segmentation, serving as the ground truth, was performed on 25 bright-blood and 15 black-blood 3D MRA image sets acquired with the iT2PrepIR-BOOST sequence (1.5T) in thoracic aortopathy patients. The training was performed with no new U-Net (nnUNet) for bright-blood (lumen) and black-blood image sets (lumen and vessel wall). Training consisted of a 70:20:10% (17/25:5/25:3/25 datasets) training:validation:testing split. Inference was run on datasets (single vendor) from different centers (UK, Spain, and Australia), sequences (iT2PrepIR-BOOST, T2 prepared coronary magnetic resonance angiography [CMRA], and time-resolved angiography with interleaved stochastic trajectories [TWIST] MRA), acquired resolutions (from 0.9-3 mm 3), and field strengths (0.55T, 1.5T, and 3T). Predictive measurements comprised Dice similarity coefficient (DSC) and Intersection over Union (IoU). Postprocessing (3D slicer) included centreline extraction, diameter measurement, and curved planar reformatting (CPR). The optimal configuration was the 3D U-Net. Bright-blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm 3) and 3D CMRA datasets (0.9 mm 3) resulted in DSC ≥ 0.96 and IoU ≥ 0.92. For bright-blood segmentation on 3D CMRA at 0.55T, the nnUNet achieved DSC and IoU scores of 0.93 and 0.88 at 1.5 mm³, and 0.68 and 0.52 at 3.0 mm³, respectively. DSC and IoU scores of 0.89 and 0.82 were obtained for CMRA image sets (1 mm 3) at 1.5T (Barcelona dataset). DSC and IoU scores of the BRnnUNet model were 0.90 and 0.82, respectively, for the contrast-enhanced dataset (TWIST MRA). Lumen segmentation on black-blood 1.5T iT2PrepIR-BOOST image sets achieved DSC ≥ 0.95 and IoU ≥ 0.90, and vessel wall segmentation resulted in DSC ≥ 0.80 and IoU ≥ 0.67. Automated centreline tracking, diameter measurement, and CPR were successfully implemented in all subjects. Automated aortic lumen and wall segmentation on 3D bright- and black-blood image sets demonstrated excellent agreement with ground truth. This technique demonstrates a fast and comprehensive assessment of aortic morphology with great potential for future clinical application in various cardiovascular diseases.
publishDate 2025
dc.date.none.fl_str_mv 2
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
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dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/326295
https://dx.doi.org/urn:doi:10.1016/j.jocmr.2025.101923
url https://ddd.uab.cat/record/326295
https://dx.doi.org/urn:doi:10.1016/j.jocmr.2025.101923
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
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dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
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instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
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