ArticleJournal of clinical medicine2021
Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning.
Article in Journal of clinical medicine, 2021. 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 accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients.Frontiers in oncology · 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
- Article
- Inter-rater and intra-rater reliability of multi-slice CT and three-dimensional reconstructed imaging analysis of mesenteric vascular anatomy for planning and performing complete mesocolic excision.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 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
- Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.The Journal of international medical research · 2024Review
- Objective Methods to Assess Aorto-Iliac Calcifications: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm.Frontiers in cardiovascular medicine · 2024Review
- Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence.Seminars in vascular surgery · 2023Review
- Convolutional neural network for automatic detection and characterization of abdominal aortic aneurysm.Journal of vascular surgery cases and innovative techniques · 2023Article
- Automatic Detection of Visceral Arterial Aneurysms on Computed Tomography Angiography Using Artificial Intelligence Based Segmentation of the Vascular System.EJVES vascular forum · 2023Article
- Abdominal Aortic Thrombus Segmentation in Postoperative Computed Tomography Angiography Images Using Bi-Directional Convolutional Long Short-Term Memory Architecture.Sensors (Basel, Switzerland) · 2022Article
- A Statistical Shape Model of the Morphological Variation of the Infrarenal Abdominal Aortic Aneurysm Neck.Journal of clinical medicine · 2022Article
- Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging.Frontiers in cardiovascular medicine · 2022Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree.
methodsWe proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert.
resultsThe hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912,
conclusionsBy enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.
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