Skip to main content

Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases

Publication ,  Conference
Kim, J; Duchin, Y; Kim, H; Vitek, J; Harel, N; Sapiro, G
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2015

This work introduces a robust framework for predicting Deep Brain Stimulation (DBS) target structures which are not identifiable on standard clinical MRI. While recent high-field MR imaging allows clear visualization of DBS target structures, such high-fields are not clinically available, and therefore DBS targeting needs to be performed on the standard clinical low contrast data. We first learn via regression models the shape relationships between DBS targets and their potential predictors from high-field (7 Tesla) MR training sets. A bagging procedure is utilized in the regression model, reducing the variability of learned dependencies. Then, given manually or automatically detected predictors on the clinical patient data, the target structure is predicted using the learned high quality information. Moreover, we derive a robust way to properly weight different training subsets, yielding higher accuracy when using an ensemble of predictions. The subthalamic nucleus (STN), the most common DBS target for Parkinson’s disease, is used to exemplify within our framework. Experimental validation from Parkinson’s patients shows that the proposed approach enables reliable prediction of the STN from the clinical 1.5T MR data.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2015

Volume

9350

Start / End Page

587 / 594

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Kim, J., Duchin, Y., Kim, H., Vitek, J., Harel, N., & Sapiro, G. (2015). Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9350, pp. 587–594). https://doi.org/10.1007/978-3-319-24571-3_70
Kim, J., Y. Duchin, H. Kim, J. Vitek, N. Harel, and G. Sapiro. “Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9350:587–94, 2015. https://doi.org/10.1007/978-3-319-24571-3_70.
Kim J, Duchin Y, Kim H, Vitek J, Harel N, Sapiro G. Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 587–94.
Kim, J., et al. “Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9350, 2015, pp. 587–94. Scopus, doi:10.1007/978-3-319-24571-3_70.
Kim J, Duchin Y, Kim H, Vitek J, Harel N, Sapiro G. Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2015. p. 587–594.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

January 1, 2015

Volume

9350

Start / End Page

587 / 594

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences