ArticleFrontiers in cardiovascular medicine2022
Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Deep learning-based segmentation of abdominal aortic aneurysms and intraluminal thrombus in 3D ultrasound images.Medical & biological engineering & computing · 2026Article
- Patient-specific digital twins in aortic disease: integrating computational hemodynamics, immune profiling, and precision endovascular strategy.Frontiers in cardiovascular medicine · 2026Review
- Automatic Segmentation of Intraluminal Thrombus in Abdominal Aortic Aneurysms Based on CT Images: A Comprehensive Review of Deep Learning-Based Methods.Journal of clinical medicine · 2025Review
- Differential effects of demographics and risk factors on the nonlinear orthotropic mechanical properties of human femoropopliteal arteries.Biomechanics and modeling in mechanobiology · 2025Article
- Simulation-Based Training in Aortic Aneurysm Surgery: Toward a New Educational Standard.Cureus · 2025Review
- Assessing differences in growth and shape between symptomatic and asymptomatic abdominal aortic aneurysms.Quantitative imaging in medicine and surgery · 2025Article
- Automatic CTA analysis for blood vessels and aneurysm features extraction in EVAR planning.Scientific reports · 2025Article
- Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model.IEEE access : practical innovations, open solutions · 2025Article
- AI-driven decision making for intravascular device selection in aortic disease. Current insights and prospects.Frontiers in cardiovascular medicine · 2025Review
- Early Prediction of Abdominal Aortic Aneurysm Rupture Risk Using Numerical Biomechanical Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- A Novel Application of Artificial Intelligence in the Management of Abdominal Aortic Aneurysm.CJC open · 2024Article
- Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.Frontiers in cardiovascular medicine · 2024Review
- AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms.JVS-vascular science · 2023Article
- 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 : official journal of the Society for Cardiovascular Magnetic ResonanceArticle
- Observer Variability in CT Angiography Carotid Segmentation: Assessing Variability to Set Minimum Clinical Performance.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Abdominal aortic aneurysm (AAA) is one of the leading causes of death worldwide. AAAs often remain asymptomatic until they are either close to rupturing or they cause pressure to the spine and/or other organs. Fast progression has been linked to future clinical outcomes. Therefore, a reliable and efficient system to quantify geometric properties and growth will enable better clinical prognoses for aneurysms. Different imaging systems can be used to locate and characterize an aneurysm; computed tomography (CT) is the modality of choice in many clinical centers to monitor later stages of the disease and plan surgical treatment. The lack of accurate and automated techniques to segment the outer wall and lumen of the aneurysm results in either simplified measurements that focus on few salient features or time-consuming segmentation affected by high inter- and intra-operator variability. To overcome these limitations, we propose a model for segmenting AAA tissues automatically by using a trained deep learning-based approach. The model is composed of three different steps starting with the extraction of the aorta and iliac arteries followed by the detection of the lumen and other AAA tissues. The results of the automated segmentation demonstrate very good agreement when compared to manual segmentation performed by an expert.
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