ArticleCardiovascular engineering and technology2020
3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks.
Article in Cardiovascular engineering and technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 79 citations in OpenAlex.
- Displacement Forces in Different Proximal Landing Zones for Thoracic Endovascular Aortic Repair: Towards Patient-Specific Assessment.Bioengineering (Basel, Switzerland) · 2026Article
- Validation of a Deep Learning U-Net Algorithm for Multistructure Segmentation of Infrarenal Abdominal Aortic Aneurysms including Lumen, Thrombus, and Calcifications.EJVES vascular forum · 2026Article
- AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation.IEEE transactions on visualization and computer graphics · 2026Article
- 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
- Automatic CTA analysis for blood vessels and aneurysm features extraction in EVAR planning.Scientific reports · 2025Article
- Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model.IEEE access : practical innovations, open solutions · 2025Article
- Automatic Explainable Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography.IEEE access : practical innovations, open solutions · 2025Article
- Assessment of the Aorto-Septal Angle Post-Thoracic Endovascular Aortic Repair through Segmentation and the Semi-Automatic Analysis of Cardiosynchronized Computed Tomography Angiography Images.Journal of cardiovascular development and disease · 2024Article
- Aorta Segmentation in 3D CT Images by Combining Image Processing and Machine Learning Techniques.Cardiovascular engineering and technology · 2024Article
- Machine learning-based identification of contrast-enhancement phase of computed tomography scans.PloS one · 2024Article
- Application of three-dimensional printing in the planning and execution of aortic aneurysm repair.Frontiers in cardiovascular medicine · 2024Review
- Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography.Journal of digital imaging · 2023Article
- Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists · 2023Article
- A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.Respiratory research · 2022Article
- Deep Learning to Automatically Segment and Analyze Abdominal Aortic Aneurysm from Computed Tomography Angiography.Cardiovascular engineering and technology · 2022Article
- Segmentation of the Aorta and Pulmonary Arteries Based on 4D Flow MRI in the Pediatric Setting Using Fully Automated Multi-Site, Multi-Vendor, and Multi-Label Dense U-Net.Journal of magnetic resonance imaging : JMRI · 2022Article
- A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography.Journal of digital imaging · 2022Article
- Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging.Frontiers in cardiovascular medicine · 2022Article
- 3D modeling and printing for complex biventricular repair of double outlet right ventricle.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence.
methodsA first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence.
resultsThe coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation.
conclusionThe achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.
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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.