Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
Coronary artery segmentation plays a key role in cardiovascular disease analysis, yet existing methods often produce fragmented and structurally inconsistent vessels in challenging regions. We propose an uncertainty-guided conservative propagation (UGCP) framework that improves segmentation reliability by allowing high-confidence regions to guide uncertain ones through controlled information propagation under a conservation principle. This mechanism enhances structural continuity while preventing unstable updates. Experiments on Coronary CT Angiography (CCTA) and Invasive Coronary Angiography (ICA) datasets demonstrate improved segmentation accuracy and topology preservation. Additional evaluations on other vascular datasets further suggest the generalizability of the proposed approach.
Included in
GRP-148-223 Uncertainty-Guided Conservative Propagation for Robust Coronary Artery Segmentation
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Coronary artery segmentation plays a key role in cardiovascular disease analysis, yet existing methods often produce fragmented and structurally inconsistent vessels in challenging regions. We propose an uncertainty-guided conservative propagation (UGCP) framework that improves segmentation reliability by allowing high-confidence regions to guide uncertain ones through controlled information propagation under a conservation principle. This mechanism enhances structural continuity while preventing unstable updates. Experiments on Coronary CT Angiography (CCTA) and Invasive Coronary Angiography (ICA) datasets demonstrate improved segmentation accuracy and topology preservation. Additional evaluations on other vascular datasets further suggest the generalizability of the proposed approach.