1. Efficient automated localization of ECoG electrodes in CT images via shape analysis
- Author
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Roberta Morace, Giancarlo Di Gennaro, Antonio Sarno, Jessica Centracchio, Marcello Bartolo, Sara Casciato, Emilio Andreozzi, Luigi Pavone, Paolo Bifulco, Vincenzo Esposito, Daniele Esposito, Centracchio, J., Sarno, A., Esposito, D., Andreozzi, E., Pavone, L., Di Gennaro, G., Bartolo, M., Esposito, V., Morace, R., Casciato, S., and Bifulco, P.
- Subjects
0301 basic medicine ,Male ,Drug Resistant Epilepsy ,Support Vector Machine ,Computer science ,Electrode ,Normal Distribution ,computer.software_genre ,Pattern Recognition, Automated ,0302 clinical medicine ,Retrospective Studie ,Voxel ,Image Processing, Computer-Assisted ,Epilepsy surgery ,Electrocorticography ,medicine.diagnostic_test ,Electroencephalography ,General Medicine ,Middle Aged ,Computer Graphics and Computer-Aided Design ,Thresholding ,Shape analysis ,Computer Science Applications ,Electrodes, Implanted ,Gaussian Support Vector Machine ,CT image processing ,Original Article ,Female ,Computer Vision and Pattern Recognition ,Human ,Shape analysis (digital geometry) ,Adult ,Similarity (geometry) ,Biomedical Engineering ,Health Informatics ,03 medical and health sciences ,Young Adult ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Electrodes ,Retrospective Studies ,Shape analysi ,business.industry ,Pattern recognition ,Electrodes recognition ,Support vector machine ,030104 developmental biology ,ElectroCorticoGraphy ,Surgery ,Artificial intelligence ,business ,Tomography, X-Ray Computed ,computer ,030217 neurology & neurosurgery ,Software - Abstract
Purpose People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients’ computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition. Methods The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes. Results The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes. Conclusions The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging. Ethical approval n°NCT04479410 by “IRCCS Neuromed” (Pozzilli, Italy), 30th July 2020.
- Published
- 2021