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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Journal of Cardiovascular Magnetic Resonance
Subjects:
Aorta
Aortic disease
Magnetic resonance angiography
Segmentation
Deep-learning
nnUNet
Online Access:http://www.sciencedirect.com/science/article/pii/S1097664725000857
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