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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
EditorsJoachim Hornegger, Alejandro F. Frangi, William M. Wells, Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, Nassir Navab, William M. Wells, William M. Wells, Alejandro F. Frangi, Joachim Hornegger, Nassir Navab
PublisherSpringer- Verlag
Pages587-594
Number of pages8
ISBN (Print)9783319245706, 9783319245706, 9783319245706
DOIs
StatePublished - Jan 1 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9350
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

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  • Cite this

    Kim, J., Duchin, Y., Kim, H., Vitek, J. L., Harel, N., & Sapiro, G. (2015). Robust prediction of clinical deep brain stimulation target structures via the estimation of influential high-field MR atlases. In J. Hornegger, A. F. Frangi, W. M. Wells, A. F. Frangi, N. Navab, J. Hornegger, N. Navab, W. M. Wells, W. M. Wells, A. F. Frangi, J. Hornegger, & N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings (pp. 587-594). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350). Springer- Verlag. https://doi.org/10.1007/978-3-319-24571-3_70