Needle localization in ultrasound images is pivotal for the successful execution of ultrasound-guided core needle biopsies. Automating the needle detection process can decrease the procedure time and lead to a more precise diagnosis. In this article, we introduce an automatic method for detecting the core needle and determining its trajectory in 2D ultrasound images. In our approach, the Vision Transformer architecture, renowned for its self-attention mechanisms is used for needle detection and segmentation, and is followed by the analysis of the Radon transformed segmentation mask to identify the needle’s trajectory. The experiments, performed over two clinical datasets of more than 600 ultrasound images rigorously split into various training-test subsets and backed up with a variety of statistical analyses revealed that our approach offers highquality needle segmentation, and significantly outperforms other techniques in identifying the needle’s trajectory, with the trajectory localization errors reduced up to more than 5× when compared to the most competitive deep learning algorithm. We believe that our work may pave the way for more accurate and efficient ultrasoundguided procedures, ultimately improving patient outcomes.