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

Jinyoung Kim; Yuval Duchin; Guillermo Sapiro; Jerrold Vitek; Noam Harel

doi:10.1109/ICIP.2015.7351248

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

Jinyoung Kim
, Yuval Duchin
, Guillermo Sapiro
, Jerrold Vitek
, Noam Harel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

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)
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	https://doi.org/10.1109/ICIP.2015.7351248
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
Volume	2015-December
ISSN (Print)	1522-4880

Other

Other	IEEE International Conference on Image Processing, ICIP 2015
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

Publisher link

10.1109/ICIP.2015.7351248

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Kim, J., Duchin, Y., Sapiro, G., Vitek, J., & Harel, N. (2015). Clinical deep brain stimulation region prediction using regression forests from high-field MRI. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings (pp. 2480-2484). Article 7351248 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December). IEEE Computer Society. https://doi.org/10.1109/ICIP.2015.7351248

Clinical deep brain stimulation region prediction using regression forests from high-field MRI. / Kim, Jinyoung; Duchin, Yuval; Sapiro, Guillermo et al.
2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society, 2015. p. 2480-2484 7351248 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2015-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, J, Duchin, Y, Sapiro, G, Vitek, J & Harel, N 2015, Clinical deep brain stimulation region prediction using regression forests from high-field MRI. in 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings., 7351248, Proceedings - International Conference on Image Processing, ICIP, vol. 2015-December, IEEE Computer Society, pp. 2480-2484, IEEE International Conference on Image Processing, ICIP 2015, Quebec City, Canada, 9/27/15. https://doi.org/10.1109/ICIP.2015.7351248

Kim J, Duchin Y, Sapiro G, Vitek J , Harel N. Clinical deep brain stimulation region prediction using regression forests from high-field MRI. In 2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings. IEEE Computer Society. 2015. p. 2480-2484. 7351248. (Proceedings - International Conference on Image Processing, ICIP). doi: 10.1109/ICIP.2015.7351248

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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.",

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AB - 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.

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

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