Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net
ABSTRACT: Background: 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 d...
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Elsevier
2025-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664725000857 |
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| author | Matteo Cesario Simon J. Littlewood James Nadel Thomas J. Fletcher Anastasia Fotaki Carlos Castillo-Passi Reza Hajhosseiny Jim Pouliopoulos Andrew Jabbour Ruperto Olivero Jose Rodríguez-Palomares M. Eline Kooi Claudia Prieto René M. Botnar |
| author_facet | Matteo Cesario Simon J. Littlewood James Nadel Thomas J. Fletcher Anastasia Fotaki Carlos Castillo-Passi Reza Hajhosseiny Jim Pouliopoulos Andrew Jabbour Ruperto Olivero Jose Rodríguez-Palomares M. Eline Kooi Claudia Prieto René M. Botnar |
| author_sort | Matteo Cesario |
| collection | DOAJ |
| description | ABSTRACT: Background: 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. Methods: 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 mm3), 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). Results: The optimal configuration was the 3D U-Net. Bright-blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm3) and 3D CMRA datasets (0.9 mm3) 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 mm3) 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. Conclusion: 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. |
| format | Article |
| id | doaj-art-0f948d87ddc843118d3ad23e2890069f |
| institution | DOAJ |
| issn | 1097-6647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-0f948d87ddc843118d3ad23e2890069f2025-08-20T02:40:17ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472025-01-0127210192310.1016/j.jocmr.2025.101923Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-NetMatteo Cesario0Simon J. Littlewood1James Nadel2Thomas J. Fletcher3Anastasia Fotaki4Carlos Castillo-Passi5Reza Hajhosseiny6Jim Pouliopoulos7Andrew Jabbour8Ruperto Olivero9Jose Rodríguez-Palomares10M. Eline Kooi11Claudia Prieto12René M. Botnar13School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the NetherlandsSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom; Clinical Research Group, Heart Research Institute, Newtown, Australia; Cardiology Department, St. Vincent’s Hospital, Darlinghurst, AustraliaSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom; Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, ChileSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom; Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, United KingdomCardiology Department, St. Vincent’s Hospital, Darlinghurst, Australia; Victor Chang Cardiac Research Institute, Sydney, AustraliaCardiology Department, St. Vincent’s Hospital, Darlinghurst, Australia; Victor Chang Cardiac Research Institute, Sydney, AustraliaDepartment of Cardiology, Vall d′Hebron Hospital Universitari, Vall d′Hebron Barcelona Hospital Campus, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain; Cardiovascular Diseases, Vall d′Hebron Institut de Recerca (VHIR), Vall d′Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona, Bellaterra, Spain; CIBER de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, SpainDepartment of Cardiology, Vall d′Hebron Hospital Universitari, Vall d′Hebron Barcelona Hospital Campus, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona, Bellaterra, Spain; Cardiovascular Diseases, Vall d′Hebron Institut de Recerca (VHIR), Vall d′Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona, Bellaterra, Spain; CIBER de Enfermedades Cardiovasculares, Instituto de Salud Carlos III, Madrid, SpainDepartment of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, the Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the NetherlandsSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, ChileSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom; Institute of Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile; Millennium Institute for Intelligent Healthcare Engineering, Santiago, Chile; Institute for Advanced Study, Technical University of Munich, Garching, Germany; Corresponding author.ABSTRACT: Background: 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. Methods: 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 mm3), 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). Results: The optimal configuration was the 3D U-Net. Bright-blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm3) and 3D CMRA datasets (0.9 mm3) 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 mm3) 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. Conclusion: 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.http://www.sciencedirect.com/science/article/pii/S1097664725000857AortaAortic diseaseMagnetic resonance angiographySegmentationDeep-learningnnUNet |
| spellingShingle | Matteo Cesario Simon J. Littlewood James Nadel Thomas J. Fletcher Anastasia Fotaki Carlos Castillo-Passi Reza Hajhosseiny Jim Pouliopoulos Andrew Jabbour Ruperto Olivero Jose Rodríguez-Palomares M. Eline Kooi Claudia Prieto René M. Botnar Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net Journal of Cardiovascular Magnetic Resonance Aorta Aortic disease Magnetic resonance angiography Segmentation Deep-learning nnUNet |
| title | 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_short | 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 |
| topic | Aorta Aortic disease Magnetic resonance angiography Segmentation Deep-learning nnUNet |
| url | http://www.sciencedirect.com/science/article/pii/S1097664725000857 |
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