Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has shown clin. potential for relieving the motor symptoms of advanced Parkinson's disease.While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clin. MR protocols is still challenging.Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN.However, MER require significant neurosurgical expertise and lengthen the surgery time.Recent advances in 7T MR technol. facilitate the ability to clearly visualize the STN.The vast majority of centers, however, still do not have 7T MRI systems, and fewer have the ability to collect and analyze the data.This work introduces an automatic STN localization framework based on standard clin. MRIs without addnl. cost in the current DBS planning protocol.Our approach benefits from a large database of 7T MRI and its clin. MRI pairs.We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors).Given a standard clin. MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN.We validate our proposed method on clin. T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs.The exptl. results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases.We also demonstrate the clin. feasibility of the proposed technique assessing the post-operative electrode active contact locations.