ArticleEuropean radiology2022
Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 35 citations in OpenAlex.
- Automated placement of the bolus-tracking region of interest using U-Net-based aortic root segmentation in coronary CT angiography.Radiological physics and technology · 2026Article
- From Bits to Bedside: From Data to Knowledge in Vascular Surgery.Journal of clinical medicine · 2026Review
- Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session.Bioengineering (Basel, Switzerland) · 2026Article
- Deep learning-driven false-lumen volumes predict adverse remodeling better than diameter in patients with residual aortic dissection on CT.European radiology · 2026Article
- A novel morphological assessment approach for abdominal aortic aneurysms: automated segmentation and measurement using a semi-supervised deep learning algorithm.Frontiers in cardiovascular medicine · 2026Article
- Abdominal Aortic Aneurysm Pathomechanics, Current Understanding and Emerging Directions: A Perspective for 2025 and Beyond.Advances in experimental medicine and biology · 2026Review
- A Fully Automatic Pipeline of Identification, Segmentation, and Subtyping of Aortic Dissection from CT Angiography.Cardiovascular engineering and technology · 2025Article
- Impact of Radiologist Experience on AI Annotation Quality in Chest Radiographs: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Artificial intelligence-powered solutions for automated aortic diameter measurement in computed tomography: a narrative review.Annals of translational medicine · 2024Review
- NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations.The international journal of cardiovascular imaging · 2024Article
- Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.Diagnostics (Basel, Switzerland) · 2024Article
- Assessing aortic motion with automated 3D cine balanced steady state free precession cardiovascular magnetic resonance segmentation.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- Fully automated pipeline for measurement of the thoracic aorta using joint segmentation and localization neural network.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.European radiology experimental · 2023Article
- A benchmark study of convolutional neural networks in fully automatic segmentation of aortic root.Frontiers in bioengineering and biotechnology · 2023Article
- An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta.European heart journal. Digital health · 2022Article
- Percutaneous Thrombectomy in Patients with Occlusions of the Aortoiliac Segment: A Case Series.Cardiovascular and interventional radiology · 2022Article
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop and validate a deep learning-based algorithm for segmenting and quantifying the physiological and diseased aorta in computed tomography angiographies.
methodsCTA exams of the aorta of 191 patients (68.1 ± 14 years, 128 male), performed between 2015 and 2018, were retrospectively identified from our imaging archive and manually segmented by two investigators. A 3D U-Net model was trained on the data, which was divided into a training, a validation, and a test group at a ratio of 7:1:2. Cases in the test group (n = 41) were evaluated to compare manual and automatic segmentations. Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff surface distance (HSD) were extracted. Maximum diameter, effective diameter, and area were quantified and compared between both segmentations at eight anatomical landmarks, and at the maximum area of an aneurysms if present (n = 14). Statistics included error calculation, intraclass correlation coefficient, and Bland-Altman analysis.
resultsA DSC of 0.95 [0.94; 0.95] and an MSD of 0.76 [0.06; 0.99] indicated close agreement between segmentations. HSD was 8.00 [4.47; 10.00]. The largest absolute errors were found in the ascending aorta with 0.8 ± 1.5 mm for maximum diameter and at the coeliac trunk with - 30.0 ± 81.6 mm
conclusionsAutomated segmentation of the aorta on CTA data using a deep learning algorithm is feasible and allows for accurate quantification of the aortic lumen even if the vascular architecture is altered by disease. KEY POINTS: • A deep learning-based algorithm can automatically segment the aorta, mostly within acceptable margins of error, even if the vascular architecture is altered by disease. • Quantifications performed in the segmentations were mostly within clinically acceptable limits, even in pathologically altered segments of the aorta.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.