The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

Roan A. LaPlante; Wei Tang; Noam Peled; Deborah I. Vallejo; Mia Borzello; Darin D. Dougherty; Emad N. Eskandar; Alik S. Widge; Sydney S. Cash; Steven M. Stufflebeam

doi:10.1007/s11548-016-1504-2

The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

Roan A. LaPlante, Wei Tang, Noam Peled, Deborah I. Vallejo, Mia Borzello, Darin D. Dougherty, Emad N. Eskandar, Alik S. Widge, Sydney S. Cash, Steven M. Stufflebeam

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Purpose: Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods: We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results: We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions: The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.

Original language	English (US)
Pages (from-to)	1829-1837
Number of pages	9
Journal	International Journal of Computer Assisted Radiology and Surgery
Volume	12
Issue number	10
DOIs	https://doi.org/10.1007/s11548-016-1504-2
State	Published - Oct 1 2017
Externally published	Yes

Keywords

CT
Electrocorticography
Image registration
MRI
Software

ASJC Scopus subject areas

Surgery
Biomedical Engineering
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Science Applications
Computer Graphics and Computer-Aided Design

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10.1007/s11548-016-1504-2

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LaPlante, R. A., Tang, W., Peled, N., Vallejo, D. I., Borzello, M., Dougherty, D. D., Eskandar, E. N., Widge, A. S., Cash, S. S., & Stufflebeam, S. M. (2017). The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes. International Journal of Computer Assisted Radiology and Surgery, 12(10), 1829-1837. https://doi.org/10.1007/s11548-016-1504-2

LaPlante, RA, Tang, W, Peled, N, Vallejo, DI, Borzello, M, Dougherty, DD, Eskandar, EN, Widge, AS, Cash, SS & Stufflebeam, SM 2017, 'The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes', International Journal of Computer Assisted Radiology and Surgery, vol. 12, no. 10, pp. 1829-1837. https://doi.org/10.1007/s11548-016-1504-2

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title = "The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes",

abstract = "Purpose: Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods: We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results: We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions: The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.",

keywords = "CT, Electrocorticography, Image registration, MRI, Software",

author = "LaPlante, {Roan A.} and Wei Tang and Noam Peled and Vallejo, {Deborah I.} and Mia Borzello and Dougherty, {Darin D.} and Eskandar, {Emad N.} and Widge, {Alik S.} and Cash, {Sydney S.} and Stufflebeam, {Steven M.}",

note = "Funding Information: We thank Giovanni Piantoni for his assistance with hardware and technical support. We thank Kristen K. Ellard, Samuel Zorowitz, Tatiana Sitnikova, Afsana Afzal, Anna L. Gilmour, Amanda R. Arulpragasam, and Thilo Deckersbach for their assistance with data collection. This work was made possible by grants NCRR S10RR014978, NIH S10RR031599, R01-NS069696, 5RO1-NS060918, U01MH093765, 1S10RR023043, 1S10RR023401, P41-EB015896. This work was sponsored by the U.S. Army Research Office and Defense Advanced Research Projects Agency under Cooperative Agreement Number W911NF-14-2-0045. The aforementioned sponsor played no role in collection, analysis, or interpretation of data, and played no role in preparation of the manuscript. Publisher Copyright: {\textcopyright} 2016, CARS.",

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doi = "10.1007/s11548-016-1504-2",

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T2 - software for automatic sorting and labeling of intracranial subdural electrodes

AU - LaPlante, Roan A.

AU - Tang, Wei

AU - Peled, Noam

AU - Vallejo, Deborah I.

AU - Borzello, Mia

AU - Dougherty, Darin D.

AU - Eskandar, Emad N.

AU - Widge, Alik S.

AU - Cash, Sydney S.

AU - Stufflebeam, Steven M.

N1 - Funding Information: We thank Giovanni Piantoni for his assistance with hardware and technical support. We thank Kristen K. Ellard, Samuel Zorowitz, Tatiana Sitnikova, Afsana Afzal, Anna L. Gilmour, Amanda R. Arulpragasam, and Thilo Deckersbach for their assistance with data collection. This work was made possible by grants NCRR S10RR014978, NIH S10RR031599, R01-NS069696, 5RO1-NS060918, U01MH093765, 1S10RR023043, 1S10RR023401, P41-EB015896. This work was sponsored by the U.S. Army Research Office and Defense Advanced Research Projects Agency under Cooperative Agreement Number W911NF-14-2-0045. The aforementioned sponsor played no role in collection, analysis, or interpretation of data, and played no role in preparation of the manuscript. Publisher Copyright: © 2016, CARS.

PY - 2017/10/1

Y1 - 2017/10/1

N2 - Purpose: Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods: We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results: We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions: The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.

AB - Purpose: Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods: We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results: We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions: The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.

KW - CT

KW - Electrocorticography

KW - Image registration

KW - MRI

KW - Software

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ER -

The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

Abstract

Keywords

ASJC Scopus subject areas

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