Automatic segmentation of stereoelectroencephalography (SEEG) electrodes post-implantation considering bending
- Submitting institution
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King's College London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 102685369
- Type
- D - Journal article
- DOI
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10.1007/s11548-018-1740-8
- Title of journal
- International Journal of Computer Assisted Radiology and Surgery
- Article number
- -
- First page
- 935
- Volume
- 13
- Issue
- 6
- ISSN
- 1861-6410
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2018
- URL
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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13
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We presented an image analysis algorithm to precisely determine the location of surgical screws and electrode contacts on post-implantation CT. We demonstrated in 224 trajectories our algorithm automatically located these objects with sub-voxel resolution (0.5 mm3) compared to manually determined locations. This manuscript led to one patent application (PCT #1807041.7). This method has been applied to automated quality assessment of neurosurgical interventions (https://doi.org/10.1016/j.jneumeth.2020.108710 ) and adopted for evaluating intracranial electrode placement in a randomised clinical trial (ISRCTN17209025) undertaken at the National Hospital for Neurology and Neurosurgery.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -