Validation of automatic cochlear implant electrode localization techniques using $\mu \mathrm{CTs}$

Cochlear implants (CIs) are standard treatment for patients who experience sensorineural hearing loss. Although these devices have been remarkably successful at restoring hearing, it is rare that they permit to achieve natural fidelity and many patients experience poor outcomes. Our group has developed image-guided CI programming techniques (IGCIP), in which image analysis techniques are used to locate the intracochlear position of CI electrodes to determine patient-customized settings for the CI processor. Clinical studies have shown that IGCIP leads to significantly improved outcomes. A crucial step is the localization of the electrodes, and rigorously quantifying the accuracy of our algorithms requires dedicated datasets. We discuss the creation of a ground truth dataset for electrode position and its use to evaluate the accuracy of our electrode localization techniques. Our final ground truth dataset includes 30 temporal bone specimens that were each implanted with one of four different types of electrode array by an experienced CI surgeon. The arrays were localized in conventional CT images using our automatic methods and manually in high-resolution $\mu \mathrm{CT}$ images to create the ground truth. The conventional and $\mu \mathrm{CT}$ images were registered to facilitate comparison between automatic and ground truth electrode localization results. Our technique resulted in mean errors of 0.13 mm in localizing the electrodes across 30 cases. Our approach successfully permitted characterizing the accuracy of our methods, which is critical to understand their limitations for use in IGCIP.