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