Clinical deep brain stimulation region prediction using regression forests from high-field MRI

Published

Conference Paper

© 2015 IEEE. This paper presents a prediction framework of brain subcortical structures which are invisible on clinical low-field MRI, learning detailed information from ultrahigh-field MR training data. Volumetric segmentation of Deep Brain Stimulation (DBS) structures within the Basal ganglia is a prerequisite process for reliable DBS surgery. While ultrahigh-field MR imaging (7 Tesla) allows direct visualization of DBS targeting structures, such ultrahigh-fields are not always clinically available, and therefore the relevant structures need to be predicted from the clinical data. We address the shape prediction problem with a regression forest, non-linearly mapping predictors to target structures with high confidence, exploiting ultrahigh-field MR training data. We consider an application for the subthalamic nucleus (STN) prediction as a crucial DBS target. Experimental results on Parkinson's patients validate that the proposed approach enables reliable estimation of the STN from clinical 1.5T MRI.

Full Text

Duke Authors

Cited Authors

  • Kim, J; Duchin, Y; Sapiro, G; Vitek, J; Harel, N

Published Date

  • December 9, 2015

Published In

Volume / Issue

  • 2015-December /

Start / End Page

  • 2480 - 2484

International Standard Serial Number (ISSN)

  • 1522-4880

International Standard Book Number 13 (ISBN-13)

  • 9781479983391

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2015.7351248

Citation Source

  • Scopus