Deep learning-based segmentation of coronary arteries in X-ray angiography supports stenosis assessment via the Quantitative Flow Ratio. However, traditional metrics like the Dice coefficient neglect howimage acquisition parameters, particularly vessel type and projection angle, affect model accuracy. This study evaluated four U-Net-based models (vanilla U-Net, nnUNet, and U-Nets using MobileNetV2 or InceptionResNetV2 as encoders) on 599 patients covering twelve common projection angles. Results show that projection angles, including vessel overlap, have a stronger impact on segmentation quality than vessel type. InceptionResNetV2 achieved the highest overall Dice scores, while nnU-Net better captured capillaries and catheters. Distal branches remained challenging for all models. Our findings highlight the need to consider projection-angle diversity and segment-level evaluation in datasets and benchmarks to ensure clinically reliable coronary segmentation.