ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids
| Autor: | Mariana P. Branco, Daniel R. Glen, Anna Gaglianese, Nick F. Ramsey, Ziad S. Saad, Dora Hermes, Natalia Petridou |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male 0301 basic medicine Drug Resistant Epilepsy Adolescent Computer science Neuroscience(all) High density Article Pattern Recognition Automated Upsampling Young Adult 03 medical and health sciences Imaging Three-Dimensional ALICE Clinical grid 0302 clinical medicine Software Image Interpretation Computer-Assisted medicine Humans Computer vision Child MATLAB computer.programming_language Cerebral Cortex Electrode localization Integrated pipeline medicine.diagnostic_test business.industry General Neuroscience Magnetic resonance imaging Middle Aged Grid ECoG Electrodes Implanted Visualization 030104 developmental biology Electrode Female Electrocorticography Artificial intelligence High-density grid Tomography X-Ray Computed business computer 030217 neurology & neurosurgery |
| Zdroj: | Journal of Neuroscience Methods, 301, 43. Elsevier |
| ISSN: | 0165-0270 |
| DOI: | 10.1016/j.jneumeth.2017.10.022 |
| Popis: | Background Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject’s neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT. New method ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual’s cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab ® , thus providing an easy, automated and standard tool for ECoG electrode localization. Results ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method. Comparison with existing methods A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects. Conclusions The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart. |
| Databáze: | OpenAIRE |
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