Efficient volume-based localization and automatic labeling of intracranial depth electrodes.

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Accurate localization and anatomical labeling of intracranial depth electrodes are critical for stereoelectroencephalography (SEEG) and epilepsy patient data interpretation.
• Current laborious electrode localization procedures necessitate an efficient and user-friendly pipeline.
• The Depth Electrode Localizer (DELLO) tool was developed to automate the identification and labeling of depth electrode contacts.

Purpose of the Study:

• To develop and validate an efficient, semi-automatic tool for localizing and labeling intracranial depth electrodes.
• To streamline the process of stereoelectroencephalography (SEEG) data acquisition and analysis.
• To improve the accuracy and ease of anatomical labeling of depth electrode contacts.

Main Methods:

• The DELLO is an open-source MATLAB package utilizing preoperative MRI and postoperative CT coregistration.
• Key steps include intensity threshold electrode spatial sampling, hierarchical clustering, and automated anatomical labeling (AAL).
• A graphical user interface (GUI) allows for result review, with minimal manual input for electrode tip/entry points and contact group naming.

Main Results:

• The DELLO successfully localized 80 depth electrodes (1,030 contacts) across 7 epilepsy patients.
• 72.43% of contacts were accurately identified within gray matter.
• High accuracy rates were achieved: 95.83% for gray-matter labeling and 90.78% for AAL.

Conclusions:

• DELLO is an integrated, open-source tool for semi-automatic localization and labeling of intracranial depth electrodes.
• Its high accuracy and efficiency can significantly facilitate SEEG interpretation.
• The tool is valuable for SEEG-based cognitive neuroscience research and clinical applications.