Cardiovascular diseases are the leading cause of death worldwide, responsible for over 19.8 million deaths annually. Accurate delineation of the vessel lumen and wall is essential for quantifying vascular health, diagnosing disease, and guiding treatment. However, manual annotation, the clinical gold standard, remains time consuming, inconsistent, and prone to human bias, limiting reproducibility and comparability across studies. To address this, we present a fully automatic pipeline designed to assist and standardize vessel annotation. A multi-purpose segmentation network first provides an estimate of the outer vessel boundary, which is then refined through a gradient based algorithm that delineates the inner lumen contour. On a clinically annotated test set of n = 46 slices, the method achieved a Mean Absolute Error of 0.197 ± 0.109mm for the inner lumen and 0.284 ± 0.237mm for the outer wall. Only 12.16% of the inner and 19.97% of the outer contour lines required manual adjustment. This demonstrates the potential of the proposed approach to substantially reduce annotation effort and improve standardization for quantitative vascular analysis.