A Computer Assisted Planning System for the Placement of sEEG Electrodes in the Treatment of Epilepsy

Zombori, Gergely; Rodionov, Roman; Nowell, Mark; Zuluaga, María A.; Clarkson, Matthew J.; Micallef, Caroline; Diehl, Beate; Wehner, Tim; Miserochi, A.; McEvoy, Andrew W.; Duncan, John S.; Ourselin, Sébastien

doi:10.1007/978-3-319-07521-1_13

Cited by 24 publications

(32 citation statements)

References 11 publications

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“…The resulting vessel probability map, ϕ(p), is used as an input of our computer assisted planning system [12]. As the electrode implanting trajectory needs to be further than a safety margin from the critical tissue (vessels in this case), the probability map serves as a measure of risk of crossing a vessel.…”

Section: Seeg Planning System Integrationmentioning

confidence: 99%

“…As the electrode implanting trajectory needs to be further than a safety margin from the critical tissue (vessels in this case), the probability map serves as a measure of risk of crossing a vessel. Within the planning system, the probability map is converted into a 3D surface mesh object, coloured using a pre-defined landmark colour scheme [12] and displayed within the neuronavigation planning system along with other critical brain structures (Figure 2(i)). …”

Section: Seeg Planning System Integrationmentioning

confidence: 99%

“…In this paper, we present a novel method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving the robustness and accuracy of vessel extraction within a computer assisted SEEG planning system [12]. The method accounts for both the scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework.…”

Section: Introductionmentioning

confidence: 99%

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

Zuluaga

Rodionov

Nowell

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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Abstract. StereoEEG implantation is performed in patients with epilepsy to determine the site of the seizure onset zone. Intracranial haemorrhage is the most common complication associated to implantation carrying a risk that ranges from 0.6 to 2.7%, with significant associated morbidity [2]. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice neurosurgeons have no assistance in the planning of the electrode trajectories. There is great interest in developing computer assisted planning systems that can optimize the safety profile of electrode trajectories, maximizing the distance to critical brain structures. In this work, we address the problem of blood vessel extraction for SEEG trajectory planning. The proposed method exploits the availability of multi-modal images within a trajectory planning system to formulate a vessel extraction framework that combines the scale and the neighbouring structure of an object. We validated the proposed method in twelve multi-modal patient image sets. The mean Dice similarity coefficient (DSC) was 0.88±0.03, representing a statistically significantly improvement when compared to the semi-automated single rater, single modality segmentation protocol used in current practice (DSC=0.78±0.02).

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Section: Seeg Planning System Integrationmentioning

confidence: 99%

Section: Seeg Planning System Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zuluaga

Rodionov

Nowell

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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“…We have developed custom-designed software for multimodality image integration, advanced 3D visualization and SEEG electrode placement planning 12,13 for the treatment of epilepsy. The emphasis is on ease of use in a clinical scenario, allowing real time use of software by clinicians, and rapid incorporation into the clinical pipeline.…”

Section: Introductionmentioning

confidence: 99%

“…The steps for image co-registration, segmentation of regions of interest, brain segmentation, extracting blood vessels from dedicated vascular imaging 15 , building 3D models, planning SEEG implantations and rapidly exporting models and plans to the operating theater are described. A novel tool is also described for automated multi-trajectory planning 13 , that increases the safety and efficacy of the implantations and substantially reduces the duration of planning. …”

Section: Introductionmentioning

confidence: 99%

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Nowell¹,

Rodionov²,

Zombori³

et al. 2016

JoVE

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Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway.

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Validation of a novel path planner for stereotactic neurosurgical interventions—A retrospective clinical study

Wankhede

Madiraju

Siampli

et al. 2022

Robotics Computer Surgery

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Background The gold standard workflow for targeting structures in the brain involves manual path planning. This preoperative manual path planning is very time‐intensive and laborious, especially when some outcome measures such as maximum ablation and penetration depth has to be optimised. Methods Our novel path planner generates an optimal path which maximises the hippocampus penetration and distance from critical structures using a precomputed cost map and a reward map. Results The average penetration ratio for 12 cases is 88.13 ± 23.23% for a resolution of 1° and a safety margin of 1 mm. Average run time for the path planner based on 1° resolution was 1.99 ± 0.68 min. Conclusions Results show that the algorithm can generate safe and clinically relevant paths with a quantitative representation of the penetration depth and is faster than the average reported time for manual path planning.

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A Computer Assisted Planning System for the Placement of sEEG Electrodes in the Treatment of Epilepsy

Cited by 24 publications

References 11 publications

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

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

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Validation of a novel path planner for stereotactic neurosurgical interventions—A retrospective clinical study

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