Fast Automated Stereo-EEG Electrode Contact Identification and Labeling Ensemble

Introduction: Stereotactic electroencephalography (SEEG) has emerged as the preferred modality for intracranial monitoring in drug-resistant epilepsy (DRE) patients being evaluated for neurosurgery. After implantation of SEEG electrodes, it is important to determine the neuroanatomic locations of electrode contacts (ECs), to localize ictal onset and propagation, and integrate functional information to facilitate surgical decisions. Although there are tools for coregistration of preoperative MRI and postoperative CT scans, identification, sorting, and labeling of SEEG ECs is often performed manually, which is resource intensive. We report development and validation of a software named Fast Automated SEEG Electrode Contact Identification and Labeling Ensemble (FASCILE). Methods: FASCILE is written in Python 3.8.3 and employs a novel automated method for identifying ECs, assigning them to respected SEEG electrodes, and labeling. We compared FASCILE with our clinical process of identifying, sorting, and labeling ECs, by computing localization error in anteroposterior, superoinferior, and lateral dimensions. We also measured mean Euclidean distances between ECs identified by FASCILE and the clinical method. We compared time taken for EC identification, sorting, and labeling for the software developer using FASCILE, a first-time clinical user using FASCILE, and the conventional clinical process. Results: Validation in 35 consecutive DRE patients showed a mean overall localization e