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Stability, structure and scale: improvements in multi-modal vessel extraction for SEEG trajectory planning
Maria A Zuluaga1, Roman Rodionov, Mark Nowell
1Translational Imaging Group, CMIC, University College London, London, UK, M.ZuluagaValencia@cs.ucl.ac.uk.
Summary
This study presents a novel computer-assisted method for brain vessel extraction to improve stereo-electroencephalography (SEEG) planning. The multi-modal approach enhances accuracy, leading to safer electrode implantation and reduced patient risk.
Area of Science:
- Neurosurgery
- Medical Imaging
- Computer-Aided Surgery
Background:
- Stereo-electroencephalography (SEEG) implantation requires precise pre-operative planning to identify safe, avascular trajectories.
- Intracranial hemorrhage is a significant complication of SEEG, highlighting the need for improved surgical risk mitigation.
- Current SEEG planning lacks computer-assisted tools for optimizing electrode paths and maximizing distance from critical structures.
Purpose of the Study:
- To develop a robust and accurate computer-assisted method for brain vessel extraction within a SEEG planning system.
- To integrate concepts of scale, neighborhood structure, and feature stability for enhanced vessel segmentation.
- To improve the safety profile of SEEG electrode trajectories by maximizing distance to critical neurovascular structures.
Main Methods:
- A multi-scale tensor voting framework was employed to account for voxel scale and vicinity.
- Feature stability was achieved using a similarity measure evaluating multi-modal consistency in vesselness responses.
- Multiple image modalities were fused into a single image for visualization of critical vessels within the planning system.
Main Results:
- The developed method achieved a mean Dice similarity coefficient of 0.89 ± 0.04 on twelve paired datasets.
- This represents a statistically significant improvement compared to the current clinical standard of semi-automated, single-modality segmentation (0.80 ± 0.03).
- The system successfully integrated data from two image modalities for comprehensive vessel visualization.
Conclusions:
- Multi-modal vessel extraction significantly outperforms semi-automated single-modality segmentation for SEEG planning.
- The proposed method offers a pathway to safer SEEG procedures by improving the accuracy of critical vessel identification.
- Enhanced vessel segmentation accuracy has the potential to reduce patient morbidity associated with SEEG implantation.
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