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Cochlear implants (CIs) are neural prosthetics that can improve hearing in patients with severe-to-profound hearing loss. CIs induce hearing sensation by stimulating auditory nerve fibers (ANFs) using an electrode array that is surgically implanted into the cochlea. After the device is implanted, an audiologist programs the CI processor to optimize hearing performance. However, without knowing which ANFs are being stimulated by each electrode, audiologists must rely solely on patient performance to inform the programming adjustments. Patient-specific neural stimulation modeling has been proposed to provide objective information to assist audiologists with programming, but this approach requires accurate localization of ANFs in patient CT images. In this paper, we propose an automatic neural-network-based method for atlas-based localization of the ANFs. Our results show that our method is able to produce smooth ANF predictions that are more realistic than those produced by a previously proposed semi-manual localization method. Accurate and realistic ANF localizations are critical for constructing patient-specific ANF stimulation models for model guided CI programming.
Mason et al. (Mon,) studied this question.