ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Automated segmentation of thoracic aortic lumen and vessel wall on three-dimensional bright- and black-blood magnetic resonance imaging using nnU-Net.
Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Analysis of Upper Airway Morphology Using Four-Dimensional Dynamic MRI With Active Deep Learning-Based Automatic Segmentation.Journal of magnetic resonance imaging : JMRI · 2026Article
- Deep learning-based basilar artery wall and lumen segmentation from 1-mm MR vessel wall imaging.Scientific reports · 2026Article
- Aortic dissection as a disease of vascular wall homeostasis: integrating vasa vasorum-inflammation-metabolism axis for mechanistic insight and clinical translation.Frontiers in immunology · 2026Review
- Synthetically trained convolutional neural networks for time-resolved aortic segmentation of four-dimensional flow magnetic resonance imaging.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
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14 authors.
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Abstract
backgroundMagnetic 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.
methodsManual 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
resultsThe optimal configuration was the 3D U-Net. Bright-blood segmentation at 1.5T on iT2PrepIR-BOOST datasets (1.3 and 1.8 mm
conclusionAutomated 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.
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