Semi-Automatic SEEG Localization and Interactive Neuroimage Visualization in Epilepsy Patients

Currently, there exist pipelines, such as FreeSurfer [15, 16], or deep learning based models [17, 18], that automatically segment structural MRI images based on an anatomical atlas. From there, manual localization of implanted electrodes have been developed within FieldTrip [19] and img_pipe [20], but are generally optimized mainly for ECoG electrodes. In epilepsy monitoring, more and more patients are being implanted with SEEG depth electrodes, as they provide access to sub-cortical structures and the 3D network of the brain [21, 22, 23]. Currently the open-sourced tools for localizing iEEG is limited in three ways: i) not optimized for SEEG and requires manual localization, ii) running pipelines have a high learning curve, or require very special data structures and iii) do not provide a way for visualization of the SEEG in the context of the 3D brain.

In this work, we developed an open-sourced repository (https://github.com/adam2392/neuroimg_ pipeline) that abstracts automatic segmentation on the structural T1 MRI, semi-automates localization of the SEEG electrodes and visualizes SEEG electrodes within a 3D brain. We validated the accuracy of our spatial localizations with respect to a manual localization, and our anatomical assignments of SEEG electrodes on a cohort of n=40 epilepsy patients.