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 language | English (US) |
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Title of host publication | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings |
Editors | Joachim 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 |
Publisher | Springer Verlag |
Pages | 587-594 |
Number of pages | 8 |
ISBN (Print) | 9783319245706, 9783319245706, 9783319245706 |
DOIs | |
State | Published - 2015 |
Event | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: Oct 5 2015 → Oct 9 2015 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9350 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 |
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Country/Territory | Germany |
City | Munich |
Period | 10/5/15 → 10/9/15 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2015.