Year: 2025
Type: article
Abstract: The analysis of vascular structures is critical for diagnosing, monitoring, and treating vascular diseases such as aneurysms, stenosis, and vascular calcification. Traditional methods often rely on manual interpretation of imaging data, which is time-consuming, subjective, and not scalable. This work explores the application of advanced machine learning techniques to automate and enhance vascular system analysis. Our contributions include achieving state-of-the-art accuracy in vascular segmentation, developing a machine learning pipeline to automatically quantify vascular calcification in peripheral arterial disease, and designing a multi-stage machine learning system for abdominal aortic aneurysm analysis that identifies aneurysm boundaries and estimates aneurysm volume in a zero-shot setting. Leveraging both supervised and unsupervised learning, the studies presented encompass tasks such as vessel segmentation, anomaly detection, boundary localization, calcium measurement, and volume estimation from computed tomography angiography (CTA) data. Emphasis is placed on overcoming challenges in data scarcity through the use of pre-trained models, transfer learning, and rule-based systems. Results demonstrate that machine learning, when carefully integrated with domain knowledge, can deliver accurate, interpretable, and scalable tools for vascular assessment. This compilation highlights the potential of AI-driven methods to support clinical decision-making and improve vascular diagnostics in real-world settings.
Author: Rajeoni, Alireza Bagheri
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: University of South Carolina
OpenAlex ID: W7160727831