Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2512580 | Proceedings of SPIE |
Keywords | Field | DocType |
Refractory Epilepsy,RNS,Neuropace | Pattern recognition,Computer science,Artificial intelligence | Conference |
Volume | ISSN | Citations |
10951 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srijata Chakravorti | 1 | 1 | 2.39 |
| Rui Li | 2 | 311 | 19.49 |
| W Rodriguez | 3 | 0 | 0.68 |
| Robert Shults | 4 | 0 | 0.34 |
| Ashwini Sharan | 5 | 21 | 3.32 |
| Dario J. Englot | 6 | 0 | 0.68 |
| Peter E. Konrad | 7 | 78 | 12.43 |
| Pierre-françois D'haese | 8 | 43 | 7.54 |
| Benoit M. Dawant | 9 | 1388 | 223.11 |