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.
Original language | English (US) |
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Title of host publication | 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 2480-2484 |
Number of pages | 5 |
ISBN (Electronic) | 9781479983391 |
DOIs | |
State | Published - Dec 9 2015 |
Event | IEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada Duration: Sep 27 2015 → Sep 30 2015 |
Publication series
Name | Proceedings - International Conference on Image Processing, ICIP |
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Volume | 2015-December |
ISSN (Print) | 1522-4880 |
Other
Other | IEEE International Conference on Image Processing, ICIP 2015 |
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Country/Territory | Canada |
City | Quebec City |
Period | 9/27/15 → 9/30/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Deep brain stimulation
- regression forests
- statistical shape models
- ultrahigh-field MRI