DISSERTATION DFENSE
Department of Computer Science and Engineering

Author : Alireza Bagheri Rajeoni
Advisor: Dr. Homayoun Valafar
Date: Sep 24th, 2025
Time: 11:00 am
Place: Rm 2267, Storey Innovation Building

Abstract

The analysis of vascular structures is critical for diagnosing, monitoring, and treating cardiovascular 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 machine learning techniques to automate and enhance vascular system analysis across multiple research efforts. 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.