ArticleCardiovascular engineering and technology2022
Deep Learning to Automatically Segment and Analyze Abdominal Aortic Aneurysm from Computed Tomography Angiography.
Article in Cardiovascular engineering and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 45 citations in OpenAlex.
- From Bits to Bedside: From Data to Knowledge in Vascular Surgery.Journal of clinical medicine · 2026Review
- Multi-centre Validation of Automated Outer-to-Outer Wall Segmentation of Infrarenal Abdominal Aortic Aneurysms Using a nnU-Net Based Model.Journal of imaging informatics in medicine · 2026Article
- Pyramid Xception network for accurate detection of abdominal abnormalities in CT scans.Scientific reports · 2026Article
- Use of artificial intelligence-assisted measurement of aortic diameter in clinical decision making about abdominal aortic aneurysm repair.Abdominal radiology (New York) · 2026Article
- Deep learning based volumetric analysis of infrarenal abdominal aortic aneurysms characterized on CTA.NPJ digital medicine · 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
- 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
- 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
- Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.Scientific reports · 2025Article
- Deep Learning-Based Aortic Diameter Measurement in Traumatic Hemorrhage Using Shallow Attention Network: A Path Forward.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic CTA analysis for blood vessels and aneurysm features extraction in EVAR planning.Scientific reports · 2025Article
- 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
- Automatic Explainable Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography.IEEE access : practical innovations, open solutions · 2025Article
- A Statistical Shape Model of Infrarenal Aortic Necks in Patients With and Without Late Type Ia Endoleak After Endovascular Aneurysm Repair.Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists · 2024Article
- Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (MComputers in biology and medicine · 2024Article
- Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease.Biomimetics (Basel, Switzerland) · 2024Review
- Automated detection of incidental abdominal aortic aneurysms on computed tomography.Abdominal radiology (New York) · 2024Article
- Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.Frontiers in cardiovascular medicine · 2024Review
- Application of three-dimensional printing in the planning and execution of aortic aneurysm repair.Frontiers in cardiovascular medicine · 2024Review
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAlthough segmentation of Abdominal Aortic Aneurysms (AAA) thrombus is a crucial step for both the planning of endovascular treatment and the monitoring of the intervention's outcome, it is still performed manually implying time consuming operations as well as operator dependency. The present paper proposes a fully automatic pipeline to segment the intraluminal thrombus in AAA from contrast-enhanced Computed Tomography Angiography (CTA) images and to subsequently analyze AAA geometry.
methodsA deep-learning-based pipeline is developed to localize and segment the thrombus from the CTA scans. The thrombus is first identified in the whole sub-sampled CTA, then multi-view U-Nets are combined together to segment the thrombus from the identified region of interest. Polygonal models are generated for the thrombus and the lumen. The lumen centerline is automatically extracted from the lumen mesh and used to compute the aneurysm and lumen diameters.
resultsThe proposed multi-view integration approach returns an improvement in thrombus segmentation with respect to the single-view prediction. The thrombus segmentation model is trained over a training set of 63 CTA and a validation set of 8 CTA scans. By comparing the thrombus segmentation predicted by the model with the ground truth data, a Dice Similarity Coefficient (DSC) of 0.89 ± 0.04 is achieved. The AAA geometry analysis provided an Intraclass Correlation Coefficient (ICC) of 0.92 and a mean-absolute difference of 3.2 ± 2.4 mm, for the measurements of the total diameter of the aneurysm. Validation of both thrombus segmentation and aneurysm geometry analysis is performed over a test set of 14 CTA scans.
conclusionThe developed deep learning models can effectively segment the thrombus from patients affected by AAA. Moreover, the diameters automatically extracted from the AAA show high correlation with those manually measured by experts.
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