Automatic localization of needles in real-time images can facilitate MR-guided percutaneous interventions. It enables automatic slice repositioning and targeting support and, thus, allows for faster workflows. Systematic acquisition of training data for deep learning tasks in the context of interventional MRI can be difficult due to the fact, that treatment quality must not be impaired. Therefore, we investigated whether images of porcine animal experiments can be used to train deep learning algorithms for needle artifact segmentation in human datasets. Results show that transfer is feasible at 1.5T. Additional fine tuning using small amounts of human data further reduces the error.