Towards Fixing Vessel Segmentation Breakage: A Systematic Review.

Background/Objectives: Accurate arterial tree reconstruction from computed tomography angiography (CTA) is essential for vascular diagnosis, surgical planning, and hemodynamic modelling. A persistent and underappreciated problem is vessel discontinuity: thin distal branches appear as disconnected fragments rather than continuous structures, caused by bifurcations, image noise, contrast variation, arterial plaque, motion artifacts, and partial volume effects. This scoping review aimed to systematically characterize computational approaches addressing vessel breakage in CTA segmentation and identify methodological gaps warranting further investigation. Methods: This scoping review was conducted in accordance with PRISMA-ScR guidelines. Google Scholar and PubMed were queried for studies published between March 2000 and March 2026. Eligible studies included peer-reviewed journal articles and conference proceedings presenting original methodological contributions to three-dimensional vascular segmentation from CTA. Results: Three generations of computational solutions were identified: classical geometric and geodesic methods, deep learning approaches with topology-aware training, and hybrid post-processing frameworks. Topology-sensitive metrics (clDice, Topology Sensitivity) were identified as preferred metrics to better capture clinical utility than standard voxel-based metrics such as the Dice coefficient. Conclusions: Vessel discontinuity remains a clinically relevant and challenge in vascular CTA segmentation. Hybrid post-processing frameworks combining deep learning with geodesic connectivity restoration represent the current state of the art. Standardized adoption of topology-aware evaluation metrics is recommended to better reflect clinical utility in future studies.