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8 March 2019 Validation of an automatic algorithm to identify NeuroPace depth leads in CT images
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Abstract
Responsive neurostimulation (RNS) is a novel surgical intervention for treating medically refractory epilepsy. A neurostimulator implanted under the skull monitors brain activity in one or two seizure foci and provides direct electrical stimulation using implanted electrodes to prevent partial onset seizures. Despite significant successes in reducing seizure frequency over time, outcomes are less than optimal in a number of patients. To maximize treatment efficacy, it is critical to identify the factors that contribute to the variance in outcomes, including accurate knowledge of the final electrode location. However, there is as yet no automated algorithm to localize the RNS electrodes in the brain. Currently, physicians manually demarcate the positions of the leads in postoperative images, a method that is affected by rater bias and is impractical for largescale studies. In this paper, we propose an intensity feature based algorithm that can automatically identify the electrode positions in postoperative CT images. We also validate the performance of our algorithm on a multicenter dataset of 13 implanted patients and test how it compares with expert raters.
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Srijata Chakravorti, Rui Li, William Rodriguez, Robert Shults, Ashwini Sharan, Dario J. Englot, Peter E. Konrad, Pierre-François D'Haese, and Benoit M. Dawant "Validation of an automatic algorithm to identify NeuroPace depth leads in CT images ", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512D (8 March 2019); https://doi.org/10.1117/12.2512580
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