Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending

Alejandro Granados*, Vejay Vakharia, Roman Rodionov, Martin Schweiger, Sjoerd B. Vos, Aidan G. O’Keeffe, Kuo Li, Chengyuan Wu, Anna Miserocchi, Andrew W. McEvoy, Matthew J. Clarkson, John S. Duncan, Rachel Sparks, Sébastien Ourselin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)
109 Downloads (Pure)

Abstract

Purpose: The accurate and automatic localisation of SEEG electrodes is crucial for determining the location of epileptic seizure onset. We propose an algorithm for the automatic segmentation of electrode bolts and contacts that accounts for electrode bending in relation to regional brain anatomy. Methods: Co-registered post-implantation CT, pre-implantation MRI, and brain parcellation images are used to create regions of interest to automatically segment bolts and contacts. Contact search strategy is based on the direction of the bolt with distance and angle constraints, in addition to post-processing steps that assign remaining contacts and predict contact position. We measured the accuracy of contact position, bolt angle, and anatomical region at the tip of the electrode in 23 post-SEEG cases comprising two different surgical approaches when placing a guiding stylet close to and far from target point. Local and global bending are computed when modelling electrodes as elastic rods. Results: Our approach executed on average in 36.17 s with a sensitivity of 98.81% and a positive predictive value (PPV) of 95.01%. Compared to manual segmentation, the position of contacts had a mean absolute error of 0.38 mm and the mean bolt angle difference of 0. 59 resulted in a mean displacement error of 0.68 mm at the tip of the electrode. Anatomical regions at the tip of the electrode were in strong concordance with those selected manually by neurosurgeons, ICC(3 , k) = 0.76 , with average distance between regions of 0.82 mm when in disagreement. Our approach performed equally in two surgical approaches regardless of the amount of electrode bending. Conclusion: We present a method robust to electrode bending that can accurately segment contact positions and bolt orientation. The techniques presented in this paper will allow further characterisation of bending within different brain regions.

Original languageEnglish
Pages (from-to)935-946
Number of pages12
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume13
Issue number6
Early online date7 May 2018
DOIs
Publication statusPublished - Jun 2018

Keywords

  • Automatic segmentation
  • Bending
  • Epilepsy
  • SEEG

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