SEEG Trajectory Planning: Combining Stability, Structure and Scale in Vessel Extraction

Zuluaga, María A.; Rodionov, Roman; Nowell, Mark; Achhala, Sufyan; Zombori, Gergely; Cardoso, M. Jorge; Miserocchi, Anna; McEvoy, Andrew W.; Duncan, John S.; Ourselin, Sébastien

doi:10.1007/978-3-319-10470-6_81

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…CT angiography and 3D phase-contrast images were processed using vessel extraction software available on EpiNav. 12 Gray matter, surface sulcal, and cortical segmentations were derived from T1-weighted MRI using Freesurfer software (version 5.0.0, Martinos Centre for Biomedical Imaging). Scalp exclusion masks were generated based on the T1-weighted MRI using basic imaging tools in EpiNav and MeshLab (version 1.3.3, University of Pisa).…”

Section: Methodsmentioning

confidence: 99%

Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy

Nowell

Sparks

Zombori

et al. 2016

JNS

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S tereoelectroencephalography (SEEG) is the recording of the brain's electrical activity through depth electrodes implanted in the parenchyma of the brain. SEEG is indicated in select patients with pharmacoresistant focal epilepsy to delineate the irritative and seizure onset zones and their relationship to eloquent cortical areas and white matter tracts.5 SEEG can be implemented with frame-based and frameless methods of implantation. 2,8 SEEG planning is a necessary prerequisite to implementation. A number of factors are considered when planning an electrode arrangement for SEEG (Table 1). Individual electrodes should enter at the crown of the gyrus to avoid sulcal boundaries and should avoid vascular structures. Furthermore, the electrode should enter at a reasonable angle perpendicular to the skull to enable the trajectory to be drilled with precision.abbreviatioNs CAP = computer-assisted planning software; IQR = interquartile range; SEEG = stereoelectroencephalography. obJective The objective of this study was to evaluate the clinical utility of multitrajectory computer-assisted planning software (CAP) to plan stereoelectroencephalography (SEEG) electrode arrangements. methods A cohort of 18 patients underwent SEEG for evaluation of epilepsy at a single center between August 2013 and August 2014. Planning of electrodes was performed manually and stored using EpiNav software. CAP was developed as a planning tool in EpiNav. The user preselects a set of cerebral targets and optimized trajectory constraints, and then runs an automated search of potential scalp entry points and associated trajectories. Each trajectory is associated with metrics for a safety profile, derived from the minimal distance to vascular structures, and an efficacy profile, derived from the proportion of depth electrodes that are within or adjacent to gray matter. CAP was applied to the cerebral targets used in the cohort of 18 previous manually planned implantations to generate new multitrajectory implantation plans. A comparison was then undertaken for trajectory safety and efficacy. results CAP was applied to 166 electrode targets in 18 patients. There were significant improvements in both the safety profile and efficacy profile of trajectories generated by CAP compared with manual planning (p < 0.05). Three independent neurosurgeons assessed the feasibility of the trajectories generated by CAP, with 131 (78.9%) of 166 trajectories deemed suitable for implementation in clinical practice. CAP was performed in real time, with a median duration of 8 minutes for each patient, although this does not include the time taken for data preparation. coNclusioNs CAP is a promising tool to plan SEEG implantations. CAP provides feasible depth electrode arrangements, with quantitatively greater safety and efficacy profiles, and with a substantial reduction in duration of planning within the 3D multimodality framework.

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Section: Methodsmentioning

confidence: 99%

Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy

Nowell

Sparks

Zombori

et al. 2016

JNS

Self Cite

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“…9 A multiscale approach based on tensor voting was also used. 11 Despite the good results of the previous studies, further research is needed to increase the accuracy of vascular segmentation and, accordingly, the safety of electrode insertion. Particular attention should be given to the cortical electrode Entry Point (EP), which is the most risky and challenging point.…”

Section: Introductionmentioning

confidence: 97%

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

et al. 2017

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Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

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“…Differently from the segmentation of other organs, there is no consolidated deep learning method which has reached human performance, and the vast majority of methods still rely on more classical tech-niques (e.g. (Cetin and Unal, 2015;Klepaczko et al, 2016;Moriconi et al, 2019;Taher et al, 2020;Wang and Chung, 2020;Zhao et al, 2015;Zuluaga et al, 2014b)). This lag can be explained by two factors.…”

Section: Introductionmentioning

confidence: 99%

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

Dang

Galati

Cortese

et al. 2022

Medical Image Analysis

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SEEG Trajectory Planning: Combining Stability, Structure and Scale in Vessel Extraction

Cited by 7 publications

References 13 publications

Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy

Comparison of computer-assisted planning and manual planning for depth electrode implantations in epilepsy

Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

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