Computer-assisted planning for the insertion of stereoelectroencephalography electrodes for the investigation of drug-resistant focal epilepsy: an external validation study

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One-third of cases of focal epilepsy are drug refractory, and surgery might provide a cure. Seizure-free outcome after surgery depends on the correct identification and resection of the epileptogenic zone. In patients with no visible abnormality on MRI, or in cases in which presurgical evaluation yields discordant data, invasive stereoelectroencephalography (SEEG) recordings might be necessary. SEEG is a procedure in which multiple electrodes are placed stereotactically in key targets within the brain to record interictal and ictal electrophysiological activity. Correlating this activity with seizure semiology enables identification of the seizure-onset zone and key structures within the ictal network. The main risk related to electrode placement is hemorrhage, which occurs in 1% of patients who undergo the procedure. Planning safe electrode placement for SEEG requires meticulous adherence to the following: 1) maximize the distance from cerebral vasculature, 2) avoid crossing sulcal pial boundaries (sulci), 3) maximize gray matter sampling, 4) minimize electrode length, 5) drill at an angle orthogonal to the skull, and 6) avoid critical neurological structures. The authors provide a validation of surgical strategizing and planning with EpiNav, a multimodal platform that enables automated computer-assisted planning (CAP) for electrode placement with user-defined regions of interest.


Thirteen consecutive patients who underwent implantation of a total 116 electrodes over a 15-month period were studied retrospectively. Models of the cortex, gray matter, and sulci were generated from patient-specific whole-brain parcellation, and vascular segmentation was performed on the basis of preoperative MR venography. Then, the multidisciplinary implantation strategy and precise trajectory planning were reconstructed using CAP and compared with the implemented manually determined plans. Paired results for safety metric comparisons were available for 104 electrodes. External validity of the suitability and safety of electrode entry points, trajectories, and target-point feasibility was sought from 5 independent, blinded experts from outside institutions.


CAP-generated electrode trajectories resulted in a statistically significant improvement in electrode length, drilling angle, gray matter–sampling ratio, minimum distance from segmented vasculature, and risk (p < 0.05). The blinded external raters had various opinions of trajectory feasibility that were not statistically significant, and they considered a mean of 69.4% of manually determined trajectories and 62.2% of CAP-generated trajectories feasible; 19.4% of the CAP-generated electrode-placement plans were deemed feasible when the manually determined plans were not, whereas 26.5% of the manually determined electrode-placement plans were rated feasible when CAP-determined plans were not (no significant difference).


CAP generates clinically feasible electrode-placement plans and results in statistically improved safety metrics. CAP is a useful tool for automating the placement of electrodes for SEEG; however, it requires the operating surgeon to review the results before implantation, because only 62% of electrode-placement plans were rated feasible, compared with 69% of the manually determined placement plans, mainly because of proximity of the electrodes to unsegmented vasculature. Improved vascular segmentation and sulcal modeling could lead to further improvements in the feasibility of CAP-generated trajectories.

ABBREVIATIONS CAP = computer-assisted planning; DBS = deep brain stimulation; DSA = digital subtraction angiography; EEG = electroencephalography; EZ = epileptogenic zone; FOV = field of view; MDT = multidisciplinary team; MRA = MR angiography; MRV = MR venography; ROI = region of interest; SEEG = stereoelectroencephalography.

Article Information

Correspondence Vejay N. Vakharia: University College London, Institute of Neurology, London, United Kingdom.

INCLUDE WHEN CITING Published online April 13, 2018; DOI: 10.3171/2017.10.JNS171826.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

© AANS, except where prohibited by US copyright law.



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    Computer-assisted determination of electrode-placement workflow. A: Using the EpiNav strategy, module ROIs are segmented automatically from the parcellation image. In this example, the cortex (white) is semitransparent to enable visualization of the underlying middle temporal gyrus (yellow), amygdala (blue), and hippocampus (red). B: Entry and target points for the electrodes within the strategy are generated automatically based on the safety metrics defined by the user. Electrodes are indicated in the right amygdala (yellow trajectory), right anterior hippocampus (green trajectory), and right posterior mesial orbitofrontal (blue trajectory). C: A surface risk heat map on the scalp was generated for the mesial orbitofrontal electrode as an example to show the safety of potential trajectory entry points. D: Orthogonal and 3D views showing the target risk heat map was generated for the mesial orbitofrontal electrode as an example to show safe trajectory target points in the orthogonal planes. Note that only 3 electrodes are shown for clarity. A probe’s-eye view (not shown) can then be linked to the orthogonal planes for further assessment of the electrode trajectories. Figure is available in color online only.

  • View in gallery

    Left: Comparison of risks and gray/white matter (GW)–sampling ratios between CAP and manual planning for electrode placement showing a statistically significant reduction in risk and improvement in GW sampling ratios. Right: Comparison of trajectory angles, lengths, and minimum distances from segmented vessels showing a statistically significant reduction in electrode-trajectory length and drilling angle and increase in the minimum distance from vasculature with the use of CAP compared with manual planning. *p < 0.01. Figure is available in color online only.


  • 1

    Bériault SAl Subaie FCollins DLSadikot AFPike GB: A multi-modal approach to computer-assisted deep brain stimulation trajectory planning. Int J Comput Assist Radiol Surg 7:6877042012

  • 2

    Bériault SAl Subaie FMok KSadikot AFPike GB: Automatic trajectory planning of DBS neurosurgery from multi-modal MRI datasets. Med Image Comput Comput Assist Interv 14:2592662011

  • 3

    Brunenberg EJLVilanova AVisser-Vandewalle VTemel YAckermans LPlatel B: Automatic trajectory planning for deep brain stimulation: a feasibility study. Med Image Comput Comput Assist Interv 10:5845922007

  • 4

    Cardinale FCossu MCastana LCasaceli GSchiariti MPMiserocchi A: Stereoelectroencephalography: surgical methodology, safety, and stereotactic application accuracy in 500 procedures. Neurosurgery 72:3533662013

  • 5

    Cardinale FPero GQuilici LPiano MColombo PMoscato A: Cerebral angiography for multimodal surgical planning in epilepsy surgery: description of a new three-dimensional technique and literature review. World Neurosurg 84:3583672015

  • 6

    Cardoso MJModat MWolz RMelbourne ACash DRueckert D: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans Med Imaging 34:197619882015

  • 7

    Davis DHKelly PJMarsh WRKall BAGoerss SJ: Computer-assisted stereotactic biopsy of intracranial lesions in pediatric patients. Pediatr Neurosci 14:31361988

  • 8

    De Momi ECaborni CCardinale FCasaceli GCastana LCossu M: Multi-trajectories automatic planner for StereoElectroEncephaloGraphy (SEEG). Int J Comput Assist Radiol Surg 9:108710972014

  • 9

    Elias WJSansur CAFrysinger RC: Sulcal and ventricular trajectories in stereotactic surgery. J Neurosurg 110:2012072009

  • 10

    Fisher RSAcevedo CArzimanoglou ABogacz ACross JHElger CE: ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55:4754822014

  • 11

    Giorgi CBroggi GCasolino DFranzini APluchino F: Computer assisted analysis of neuroradiological data in planning neurosurgical procedures. J Neurosurg Sci 33:19221989

  • 12

    Giorgi CCasolino SDFranzini AServello DPasserini ABroggi G: Computer-assisted planning of stereotactic neurosurgical procedures. Childs Nerv Syst 5:2993021989

  • 13

    Guo TParrent AGPeters TM: Automatic target and trajectory identification for deep brain stimulation (DBS) procedures. Med Image Comput Comput Assist Interv 10:4834902007

  • 14

    Keezer MRSisodiya SMSander JW: Comorbidities of epilepsy: current concepts and future perspectives. Lancet Neurol 15:1061152016

  • 15

    Mullin JPShriver MAlomar SNajm IBulacio JChauvel P: Is SEEG safe? A systematic review and meta-analysis of stereo-electroencephalography-related complications. Epilepsia 57:3864012016

  • 16

    Nowell MRodionov RZombori GSparks RRizzi MOurselin S: A pipeline for 3D multimodality image integration and computer-assisted planning in epilepsy surgery. J Vis Exp (111):534502016

  • 17

    Nowell MRodionov RZombori GSparks RWinston GKinghorn J: Utility of 3D multimodality imaging in the implantation of intracranial electrodes in epilepsy. Epilepsia 56:4034132015

  • 18

    Nowell MSparks RZombori GMiserocchi ARodionov RDiehl B: Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy. J Neurosurg 124:182018282016

  • 19

    Nowinski WLYang GLYeo TT: Computer-aided stereotactic functional neurosurgery enhanced by the use of the multiple brain atlas database. IEEE Trans Med Imaging 19:62692000

  • 20

    Prados FCardoso MJBurgos NGandini Wheeler-Kingshott CAMOurselin S: NiftyWeb: web based platform for image processing on the cloud, presented at the ISMRM 24th Annual Meeting & Exhibition2016. ( [Accessed November 29 2017] (Abstract)

  • 21

    Shamir RRTamir IDabool EJoskowicz LShoshan Y: A method for planning safe trajectories in image-guided keyhole neurosurgery. Med Image Comput Comput Assist Interv 13:4574642010

  • 22

    Shenai MBRoss DASagher O: The use of multiplanar trajectory planning in the stereotactic placement of depth electrodes. Neurosurgery 60 (4 Suppl 2):2722762007

  • 23

    Sparks RVakharia VRodionov RVos SBDiehl BWehner T: Anatomy-driven multiple trajectory planning (ADMTP) of intracranial electrodes for epilepsy surgery. Int J Comput Assist Radiol Surg 12:124512552017

  • 24

    Sparks RZombori GRodionov RNowell MVos SBZuluaga MA: Automated multiple trajectory planning algorithm for the placement of stereo-electroencephalography (SEEG) electrodes in epilepsy treatment. Int J Comput Assist Radiol Surg 12:1231362017

  • 25

    Téllez-Zenteno JFHernández-Ronquillo LBuckley SZahagun RRizvi S: A validation of the new definition of drug-resistant epilepsy by the International League Against Epilepsy. Epilepsia 55:8298342014

  • 26

    Trope MShamir RRJoskowicz LMedress ZRosenthal GMayer A: The role of automatic computer-aided surgical trajectory planning in improving the expected safety of stereotactic neurosurgery. Int J Comput Assist Radiol Surg 10:112711402015

  • 27

    Vaillant MDavatzikos CTaylor RHBryan RN: A path-planning algorithm for image-guided neurosurgery in Troccaz JGrimson EMösges R (eds): CVRMed-MRCAS’97. Berlin: Springer1997 pp 467476

  • 28

    Vakharia VNSparks RO’Keeffe AGRodionov RMiserocchi AMcEvoy A: Accuracy of intracranial electrode placement for stereoencephalography: a systematic review and meta-analysis. Epilepsia 58:9219322017

  • 29

    Zamorano LJiang ZKadi AM: Computer-assisted neurosurgery system: Wayne State University hardware and software configuration. Comput Med Imaging Graph 18:2572711994

  • 30

    Zuluaga MARodionov RNowell MAchhala SZombori GMendelson AF: Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning. Int J Comput Assist Radiol Surg 10:122712372015




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