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Related Experiment Video

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An automated algorithm for stereoelectroencephalography electrode localization and labelling.

Simeon M Wong1, Olivia N Arski2, George M Ibrahim3

  • 1Neurosciences and Mental Health, Hospital for Sick Children, 686 Bay St, Toronto, Ontario, M5G 0A4, Canada; Institute of Biomedical Engineering, University of Toronto, 164 College St, Toronto, Ontario, M5S 3E2, Canada; Division of Neurosurgery, Hospital for Sick Children, 555 University Ave, Toronto, Ontario, M5G 1×8, Canada.

Seizure
|April 12, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated pipeline for localizing stereoelectroencephalography (sEEG) electrodes, significantly improving efficiency in analyzing brain function data. The method accurately groups and classifies electrodes, accelerating research and clinical applications.

Keywords:
CTMRIRegistrationStereoelectroencephalography

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Stereoelectroencephalography (sEEG) is crucial for epilepsy research and functional brain mapping.
  • Accurate electrode localization is essential for group analyses but remains a bottleneck.

Purpose of the Study:

  • To develop an automated algorithm for grouping and localizing sEEG electrodes.
  • To create a pipeline for processing raw CT and MRI images into standardized MNI coordinates.

Main Methods:

  • An algorithm was developed to group sEEG electrodes by trajectory, target, and insertion point.
  • A pipeline was implemented to automate electrode localization from raw CT and MRI data.
  • The system outputs atlas-labeled MNI coordinates for electrode localization.

Main Results:

  • The automated pipeline successfully processed 190 out of 196 electrode trajectories.
  • Localization accuracy was within 0.25±0.55 mm of manual annotations.
  • Interpolation methods were used for trajectories with metal artifacts.

Conclusions:

  • The developed algorithm and pipeline automate the critical manual steps of sEEG electrode localization.
  • This automation expedites the analysis of sEEG data, especially for large patient cohorts.
  • The tool enhances existing pipelines for neuroimaging research.