Abstract #3031

STN/GP-nets: Fully automatic deep-learning based segmentation for DBS applications using ultra-high 7 Tesla MRI

Oren Solomon¹, Tara Palnitkar^1,2, Rémi Patriat¹, Henry Braun¹, Joshua Aman², Michael C Park^2,3, Guillermo Sapiro⁴, Jerrold Vitek², and Noam Harel^1,3

¹Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States, ²Department of Neurology, University of Minnesota, Minneapolis, MN, United States, ³Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States, ⁴Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Department of Computer Science, Department of Mathematics, Duke University, Durham, NC, United States

Deep brain stimulation (DBS) surgery has been shown to improve the quality of life for patients with various motor dysfunctions. The success of DBS is directly related to the proper placement of the electrodes, which requires accurate detection and identification of the relevant target structures. We present a deep-learning based automatic, robust and accurate segmentation technique from 7 Tesla MRI acquisitions of subcortical structures for DBS surgery planning and post-operative electrode localization. DBS targets and related structures include the subthalamic nucleus, substantia nigra, red nucleus and the internal and external compartments of the globus pallidus.

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