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Towards machine learning prediction of deep brain stimulation (DBS) intra-operative efficacy maps
Author(s): Camilo Bermudez; William Rodriguez; Yuankai Huo; Allison E. Hainline; Rui Li; Robert Shults; Pierre D. D’Haese; Peter E. Konrad; Benoit M. Dawant; Bennett A. Landman
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Paper Abstract

Deep brain stimulation (DBS) has the potential to improve the quality of life of people with a variety of neurological diseases. A key challenge in DBS is in the placement of a stimulation electrode in the anatomical location that maximizes efficacy and minimizes side effects. Pre-operative localization of the optimal stimulation zone can reduce surgical times and morbidity. Current methods of producing efficacy probability maps follow an anatomical guidance on magnetic resonance imaging (MRI) to identify the areas with the highest efficacy in a population. In this work, we propose to revisit this problem as a classification problem, where each voxel in the MRI is a sample informed by the surrounding anatomy. We use a patch-based convolutional neural network to classify a stimulation coordinate as having a positive reduction in symptoms during surgery. We use a cohort of 187 patients with a total of 2,869 stimulation coordinates, upon which 3D patches were extracted and associated with an efficacy score. We compare our results with a registration-based method of surgical planning. We show an improvement in the classification of intraoperative stimulation coordinates as a positive response in reduction of symptoms with AUC of 0.670 compared to a baseline registration-based approach, which achieves an AUC of 0.627 (p < 0.01). Although additional validation is needed, the proposed classification framework and deep learning method appear well-suited for improving pre-surgical planning and personalize treatment strategies.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094922 (15 March 2019); doi: 10.1117/12.2509728
Show Author Affiliations
Camilo Bermudez, Vanderbilt Univ. (United States)
William Rodriguez, Vanderbilt Univ. (United States)
Yuankai Huo, Vanderbilt Univ. (United States)
Allison E. Hainline, Vanderbilt Univ. (United States)
Rui Li, Vanderbilt Univ. (United States)
Robert Shults, Vanderbilt Univ. (United States)
Pierre D. D’Haese, Vanderbilt Univ. (United States)
Peter E. Konrad, Vanderbilt Univ. Medical Ctr. (United States)
Benoit M. Dawant, Vanderbilt Univ. (United States)
Bennett A. Landman, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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