Efficient automated localization of ECoG electrodes in CT images via shape analysis

Autor: Roberta Morace, Giancarlo Di Gennaro, Antonio Sarno, Jessica Centracchio, Marcello Bartolo, Sara Casciato, Emilio Andreozzi, Luigi Pavone, Paolo Bifulco, Vincenzo Esposito, Daniele Esposito
Přispěvatelé: Centracchio, J., Sarno, A., Esposito, D., Andreozzi, E., Pavone, L., Di Gennaro, G., Bartolo, M., Esposito, V., Morace, R., Casciato, S., Bifulco, P.
Jazyk: angličtina
Rok vydání: 2021
Předmět:
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
Zdroj: International Journal of Computer Assisted Radiology and Surgery
ISSN: 1861-6429
1861-6410
Popis: 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.
Databáze: OpenAIRE
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