Deep learning for vascular segmentation and tissue characterization in CT images

Author(s):
Zhang, X. (0000-0001-6085-2844)
Published
2026
Publication type
Doctoral Thesis

Abstract

This thesis focuses on enhancing CTA image analysis and interpretation using the latest deep learning technologies. The overarching aim of this thesis is to overcome existing technical limitations in the automated analysis of CTA imaging data by developing advanced deep learning methods for two CTA application domains: coronary computed tomographic angiography (CCTA) and CTA of liver vasculature.

CTACoronary plaque detectionLiver vessel segmentationCouinaud segmentationDiffusion modelMulti-scale graph-attentionMaximum intensity projection3D graph reasoningPoint net
Defense date
2026-01-07
Language
English
Supervisor(s)
Lelieveldt, B.P.F.
Advisor(s)
Dijkstra, J.; Broersen, A.
Committee member(s)
Quax, P.H.A.; Sánchez, C.I.; Walsum, T. van; Burgmans, M.C.
Qualification
Ph.D.
Degree granting institution
Faculty of Medicine, Leiden University Medical Center (LUMC), Leiden University, Leiden
hdl
1887/4286096
doi
10.60602/1887-4286096
ISBN(s)
9789465229904