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Coronary artery segmentation from CT scans is a helpful tool for coronary artery diseases diagnosis, which is frequently characterised by a vessel narrowing (stenosis). This is a highly demanded and high time-consuming process, thus automated procedures are becoming increasingly necessary. In this work, we propose an extremely light computationally 2D UNet that uses transfer learning for the first time in CT images. We compare the results, using different architectures and backbones, of a 2D UNet and a 3D UNet trained from scratch (i.e. weights are randomly initialised) and a 2D EfficientUNet. Both the amount of input data, with a total of 88 patients, and the extension of the structure to be recognised, the aorta and the coronary arteries ( $A + C.A$ ), as well as the coronary arteries only ( $C.A$ ) are analysed. Network outputs in clinically identified stenotic lesion areas are also assessed. The results show the advantage of using transfer learning when data is scarce, improving the $F_{1}$ score by up to 0.6 points for the 2D UNet. On the other hand, when data is sufficient, $F_{1}$ score values are close to 0.9 for all the networks. Besides, the results reveal that the 2D UNet distinguishes the thinnest and most distal vessels, although in the presence of a lesion, there is a clear tendency to overestimate it. The network with the best accuracy is the 3D UNet, with values above 95% and 75% in $A+C.A$ and $C.A$ , respectively. Moreover, the proposed methods show dependence on the amount of training data and dataset structure ( $A + C.A$ or $C.A$ ).