An open-source automated platform for three-dimensional visualization of subdural electrodes using CT-MRI coregistration
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Updated: Apr 21, 2026
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
Published on: May 20, 2016
Allan A Azarion1, Jue Wu, Allison Pearce
1Neurology, Perelman School of Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A; Penn Center for Neuroengineering and Therapeutics (CNT), Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.
Objective:
Visualizing implanted subdural electrodes in three-dimensional (3D) space can greatly aid in planning, executing, and validating resection in epilepsy surgery. Coregistration software is available, but cost, complexity, insufficient accuracy, or validation limit adoption. We present a fully automated open-source application, based on a novel method using postimplant computerized tomography (CT) and postimplant magnetic resonance (MR) images, for accurately visualizing intracranial electrodes in 3D space.
Methods:
CT-MR rigid brain coregistration, MR nonrigid registration, and prior-based segmentation were carried out on seven patients. Postimplant CT, postimplant MR, and an external labeled atlas were then aligned in the same space. The coregistration algorithm was validated by manually marking identical anatomic landmarks on the postimplant CT and postimplant MR images. Following coregistration, distances between the center of the landmark masks on the postimplant MR and the coregistered CT images were calculated for all subjects. Algorithms were implemented in open-source software and translated into a "drag and drop" desktop application for Apple Mac OS X.
Results:
Despite postoperative brain deformation, the method was able to automatically align intrasubject multimodal images and segment cortical subregions, so that all electrodes could be visualized on the parcellated brain. Manual marking of anatomic landmarks validated the coregistration algorithm with a mean misalignment distance of 2.87 mm (standard deviation 0.58 mm)between the landmarks. Software was easily used by operators without prior image processing experience.
Significance:
We demonstrate an easy to use, novel platform for accurately visualizing subdural electrodes in 3D space on a parcellated brain. We rigorously validated this method using quantitative measures. The method is unique because it involves no preprocessing, is fully automated, and freely available worldwide. A desktop application, as well as the source code, are both available for download on the International Epilepsy Electrophysiology Portal (https://www.ieeg.org) for use and interactive refinement.
