RT Dissertation/Thesis
T1 Automated Segmentation and Quality Enhancement in Medical Imaging: Applications to Cardiology
A1 Serrano Antón, Belén
K1 medical imaging
K1 coronary arteries
K1 cardiology
K1 machine learning
K1 segmentation
AB Medical imaging is crucial for non-invasive diagnosis, treatment planning, and imageguided interventions, yet accurate analysis requires advanced processing techniques. This thesis focuses on automating segmentation in computed tomography (CT), specifically for coronary geometries and aortic calcifications. Automation enhances consistency, accelerates diagnosis, and enables scalable, reproducible analysis, facilitating data-driven and personalized clinical decision-making. By leveraging artificial intelligence, this work improves segmentation accuracy and addresses challenges such as CT artifacts. The integration of automated processes into clinical workflows optimizes operations, minimizes manual intervention, and enhances the reliability of medical imaging analysis in real-world applications.
YR 2025
FD 2025
LK https://hdl.handle.net/10347/42118
UL https://hdl.handle.net/10347/42118
LA eng
DS Minerva
RD 23 abr 2026