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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.