LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes

Abstract

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Using chronic recordings from a closed‐loop neurostimulation system to capture seizures across multiple thalamic nuclei

“…Relevant nuclei are labeled. Nuclei were derived using the THOMAS atlas 17 and LeGui software (see Methods section for details) 18 . AV: anterior ventral nucleus, CM: centromedian nucleus, MD‐pf: mediodorsal‐parafascicular nucleus, Pul: pulvinar nucleus., VA: ventral anterior nucleus, VPL: ventral posterior lateral nucleus, VLPd: ventral lateral posterior dorsal group.…”

“…For visualization purposes, the thalamic nuclei have been isolated using the THOMAS atlas 17 and Locate Electrodes Graphical User Interface (LeGUI) software. 18 Briefly, the patient's preoperative T1 inversion recovery MRI sequence was warped to AC‐PC space and then coregistered to the THOMAS atlas using algorithms within LeGUI software (which utilizes protocols from SPM12) that our lab developed. The contact locations within the individual thalamic nuclei were identified using this method.…”

Modeling intracranial electrodes. A simulation platform for the evaluation of localization algorithms

“…The detection of CT artifacts has typically been a manual process ( Princich et al, 2013 ), but has recently been approached by semiautomatic techniques such as clustering voxels of high intensity ( Taimouri et al, 2014 ; Blenkmann et al, 2015 , 2017 ; Brang et al, 2016 ; Qin et al, 2017 ; Branco et al, 2018a ; Granados et al, 2018 ), shape analysis ( Centracchio et al, 2021 ), or the interpolation of coordinates given entry and target points in depth electrodes ( Arnulfo et al, 2015 ; Li et al, 2020 ). Moreover, several approaches have been integrated in novel processing pipelines ( LaPlante et al, 2016 ; Blenkmann et al, 2017 ; Groppe et al, 2017 ; Stolk et al, 2018 ; Li et al, 2020 ; Davis et al, 2021 ; Rockhill et al, 2022 ), providing users several alternatives and even handling group studies ( Deman et al, 2018 ).…”

“…Accordingly, we modeled electrode artifacts after thresholding, as most algorithms are applied to this type of data. However, there might be some cases where thresholding is a fundamental step to assess (e.g., Davis et al, 2021 ). CT image augmentation and more complex models need to be developed for such instances.…”

A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy

“…We offer a hierarchical labeling of SEEG contacts that is based on precise postoperative location of depth electrode contact within patient‐specific anatomy. There have been multiple tools and pipelines developed for localization of depth electrodes in the context of SEEG using some or all of the same software reported here (Davis et al, 2021; Medina Villalon et al, 2018; Narizzano et al, 2017; Princich et al, 2013; Qin et al, 2017; Taylor et al, 2021). Each has its own advantages, such as speed and efficiency, extent of clinical validation, and detailed considerations such as accounting for electrode curve.…”