Efficient Automated Localization of ECoG Electrodes in CT Images Via Shape Analysis

For people with epilepsy (PwE) refractory to anti-seizure medication sometimes the non-invasive presurgical evaluation using ElectroEncephaloGram (EEG) recorded directly from the scalp is not sufficient to delineate the epileptogenic zone and to identificate the eloquent cortex. In these cases, an invasive approach using intracranial electroencephalography (iEEG) is needed Subdural electrodes are used frequently in the presurgical evaluation of patients who are candidates for epilepsy surgery. Electrodes placed directly on the surface of the cortex provide a signal with a much higher resolution than that provided from scalp electrodes, and have a much clear view of small loci of activity which is difficult to see on the scalp.

Subdural electrodes allow not only the localization of abnormal epileptic tissue but also the localization of adjacent normal functions. Therefore, the precise anatomical localization of the electrodes on the patient's brain plays a key role in the definition of the epileptogenic zone or in the mapping of eloquent cortex.

From a clinical point of view, the accurate localization of the anatomical boundaries of the epileptogenic zone allows to exclude eloquent areas, avoid deficits to patient and minimize brain volume resection.

The localization of these electrodes is generally obtained by matching the locations of the electrodes with the brain anatomy of the patient. Commonly, a pre-implant magnetic resonance image (MRI) is co-registered to a post-implant computed tomography scan (CT) because MRI offers higher brain tissue contrast, while CT supports electrodes localization , even if CT images are affected by metal artifacts.

Various dedicated software tools that support pre-surgical evaluation are currently available as Matlab-based packages or open source softwares, also with graphical user interfaces. They mainly provide MRI-CT co-registration and offer only basic features for recognition of ECoG electrodes from CT scans. Most dedicated softwares segment the electrodes via simple image thresholding and allow manual interaction to correct the data. Manual methods are very time consuming,user-dependent and prone to inaccuracy. On the other hand, the mere CT image thresholding method is not able to recognize all the electrodes and to completely exclude other metallic objects, such as wires, tooth filings, intracranial clips, splinters, stitches, hearing aids or intracranial stents. Hence, manual intervention is often required to adjust the data. For example, the ALICE tool considers the volume of segmented clusters to identify the electrodes, but turned out to be unable to exclude other objects with comparable volumes (e.g. wire clusters).