ArticleMachine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stenting : first International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conj...2019
Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model.
Article in Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stenting : first International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conj..., 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 15 citations in OpenAlex.
- AortaExplorer: AI-driven analysis of the aorta in CT images.Medical & biological engineering & computing · 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
- Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning.Diagnostics (Basel, Switzerland) · 2025Article
- 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
- Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Automatic segmentation of abdominal aortic aneurysms from CT angiography using a context-aware cascaded U-Net.Computers in biology and medicine · 2023Article
- Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Evaluation of a hybrid pipeline for automated segmentation of solid lesions based on mathematical algorithms and deep learning.Scientific reports · 2022Article
- Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm.Applications in engineering science · 2022Article
- Deep learning -- promises for 3D nuclear imaging: a guide for biologists.Journal of cell science · 2022Review
- Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging.Frontiers in cardiovascular medicine · 2022Article
- A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm.PeerJ. Computer science · 2022Article
- Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning.Journal of clinical medicine · 2021Article
- 3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks.Cardiovascular engineering and technology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
Abstract
An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Thus, in this work we propose a methodology to train a CNN only with images generated with a synthetic shape model, and test its generalization and ability to segment AAAs from new original CTA scans. The synthetic images are created from realistic deformations generated by applying principal component analysis to the deformation fields obtained from the registration of few datasets. The results show that the performance of a CNN trained with synthetic data to segment AAAs from new scans is comparable to the one of a network trained with real images. This suggests that the proposed methodology may be applied to generate images and train a CNN to segment other types of aneurysms, reducing the burden of obtaining large annotated image databases.
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What OpenQuestion holds
Registered trials
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.