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While accurate localization of the STN is critical for consistent across\u2010patients effective DBS, clear visualization of the STN under standard clinical 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 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient\u2010specific STN. We validate our proposed method on clinical T\n                    <jats:sub>2<\/jats:sub>\n                    W MRI of 80 subjects, comparing with experts\u2010segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient\u2010specific STN localization than using state\u2010of\u2010the\u2010art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post\u2010operative electrode active contact locations.\n                  <\/jats:p>","DOI":"10.1002\/hbm.24404","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T09:45:44Z","timestamp":1540979144000},"page":"679-698","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Automatic localization of the subthalamic nucleus on patient\u2010specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5862-0363","authenticated-orcid":false,"given":"Jinyoung","family":"Kim","sequence":"first","affiliation":[{"name":"Surgical Information Sciences, Inc.  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