@inproceedings{622503c30fe94a14826427b812120610,
title = "Clinical deep brain stimulation region prediction using regression forests from high-field MRI",
abstract = "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.",
keywords = "Deep brain stimulation, regression forests, statistical shape models, ultrahigh-field MRI",
author = "Jinyoung Kim and Yuval Duchin and Guillermo Sapiro and Jerrold Vitek and Noam Harel",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Image Processing, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",
year = "2015",
month = dec,
day = "9",
doi = "10.1109/ICIP.2015.7351248",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "2480--2484",
booktitle = "2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings",
address = "United States",
}