Abstract
PurposeThe 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.MethodsCo-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.ResultsOur 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^{circ } 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.ConclusionWe 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.
Highlights
Epilepsy is a disease characterised by an enduring predisposition to generate epileptic seizures and affects 1% of the population [8]
Entry point (EP) accuracy can be affected by misregistration of the neuronavigation system, inaccurate alignment, and deflection during drilling, whereas target point (TP) errors may be caused by the angle at which the electrode passes through skull, deflection of the electrode at the dura or within the brain, the rigidity of the electrode, and the depth to which a guiding stylet is inserted [3,21]
– Contact position Compared to the manual segmentation done by a clinical scientist (M1) and a neurosurgeon (M2), we found that the contact location of our automatic segmentation approach had a mean absolute error (MAE) of 0.38 and 0.40 mm, respectively, and a rootmean-square deviation (RMSD) of 0.45
Summary
Epilepsy is a disease characterised by an enduring predisposition to generate epileptic seizures and affects 1% of the population [8]. SEEG is a procedure in which multiple electrodes are stereotactically inserted to identify the seizure onset zone [21]. Planning of electrode implantation is crucial for avoiding blood vessel damage and subsequent intracranial haemorrhage (which occurs in 1–2% of patients), and automatic computer-assisted multiple trajectory planning tools have been proposed [17,18]. Entry point (EP) accuracy can be affected by misregistration of the neuronavigation system, inaccurate alignment, and deflection during drilling, whereas target point (TP) errors may be caused by the angle at which the electrode passes through skull, deflection of the electrode at the dura or within the brain, the rigidity of the electrode, and the depth to which a guiding stylet is inserted [3,21]. TP displacement is the main source of error and understanding why and how electrodes bend may help predict final TP positions during surgical planning and improve EZ localisation [22]
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