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Stereo-Electro-Encephalo-Graphy SEEG With Robotic Assistance in the Presurgical Evaluation of Medical Refractory Epilepsy: A Technical Note
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Super-resolution for localizing electrode grids as small, deformable objects during epilepsy surgery using augmented

Hizirwan S Salim1, Abdullah Thabit2, Sem Hoogteijling3,4

  • 1High Performance Compute & Visualization, SURF bv, Utrecht, The Netherlands. hizirwan.salim@surf.nl.

International Journal of Computer Assisted Radiology and Surgery
|June 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-powered augmented reality system for precise localization of epilepsy surgery grids. The novel method achieves sub-5mm accuracy, enhancing surgical planning for focal epilepsy treatment.

Keywords:
Artificial intelligenceAugmented realityComputer-assisted surgeryEpilepsy surgeryInstrument trackingIntra-operative electrocorticographyPose estimation

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Computer Vision

Background:

  • Epilepsy surgery offers a potential cure for focal epilepsy.
  • Accurate localization of intraoperative electrocorticogram (ioECoG) grids is crucial for successful surgical outcomes.
  • Current methods face challenges in precisely locating small, deformable ioECoG grids.

Purpose of the Study:

  • To develop and evaluate a novel method for localizing small, deformable objects, specifically ioECoG grids.
  • To assess the feasibility of using augmented reality (AR) head-mounted displays (HMDs) integrated with artificial intelligence (AI) for this task.
  • To improve the accuracy of ioECoG grid localization during epilepsy surgery.

Main Methods:

  • Developed an image processing method using the HoloLens 2 AR HMD.
  • Combined object detection, super-resolution, and pose estimation AI models with stereo triangulation.
  • Trained AI models on a synthetic dataset of 90,000 images.
  • Tested the system in a controlled environment against an optical tracker, evaluating accuracy at distances from 40 to 90 cm.

Main Results:

  • Achieved sub-5 mm accuracy in localizing the ioECoG grid at distances < 60 cm.
  • Demonstrated accuracy below 2 mm at 40 cm, with a standard deviation < 0.5 mm.
  • The system processed an average of 24 stereo frames per second at 60 cm.

Conclusions:

  • The study confirms the feasibility of using AR HMDs for localizing small, deformable objects like ioECoG grids.
  • The achieved accuracy is clinically relevant, though further validation in clinical settings is necessary.
  • Future research should focus on assessing the method's impact on surgical precision and patient outcomes.