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

Adam Li Presenter
Johns Hopkins university
Johns Hopkins university
Baltimore, MD 
United States
Software Demonstrations 
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 ( 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.