Coronary Arteries Segmentation in Invasive X-ray Angiography: A Comprehensive Review and Benchmarking

Coronary artery disease remains the leading cause of morbidity and mortality worldwide, and X-ray coronary angiography (XCA) is the gold standard for its diagnosis and management during real-time cardiac interventions. Accurate segmentation of coronary arteries in XCA is a critical step in quantitative analysis, supporting stenosis detection, treatment planning, and 3D reconstruction. However, segmentation is highly challenging due to overlapping vessels, bone shadows, low contrast, and complex vascular geometry. In this review, we provide the first comprehensive synthesis that systematically categorises and critically evaluates segmentation methods for XCA, covering both classical image processing techniques and emerging machine learning and deep learning approaches. We summarise their evolution, strengths, and limitations, and present benchmarking of advanced deep learning models on two public datasets using Dice score, sensitivity, and precision. The observed performance variability highlights the need for robust algorithms capable of addressing label scarcity, cross-domain generalisability, and interpretability. By consolidating methodological advances and benchmarking evidence, this review offers a foundation to guide future developments in reliable coronary artery segmentation for clinical translation.