Abstract
PURPOSE: Cochlear implants (CIs) are neural prosthetics used to treat patients with severe-to-profound hearing loss. Patient-specific modeling of CI stimulation of the auditory nerve fiber (ANF) can help audiologists improve the CI programming. These models require localization of the ANFs relative to the surrounding anatomy and the CI. Localization is challenging because the ANFs are so small that they are not directly visible in clinical imaging. We hypothesize that the position of the ANFs can be accurately inferred from the location of the internal auditory canal (IAC), which has high contrast in CT because the ANFs pass through this canal between the cochlea and the brain. APPROACH: Inspired by VoxelMorph, we propose a deep atlas-based IAC segmentation network. We create a single atlas in which the IAC and ANFs are pre-localized. Our network is trained to produce deformation fields (DFs) mapping coordinates from the atlas to new target volumes and that accurately segment the IAC. We hypothesize that DFs that accurately segment the IAC in target images will also facilitate accurate atlas-based localization of the ANFs. As opposed to VoxelMorph, which aims to produce DFs that accurately register the entire volume, our contribution is an entirely self-supervised training scheme that aims to produce DFs that accurately segment the target structure. This self-supervision is facilitated using a loss function inspired by the Mumford-Shah functional. We call our method Deep Atlas-Based Segmentation using Mumford-Shah (DABS-MS). RESULTS: Results show that DABS-MS outperforms VoxelMorph for IAC segmentation. Tests with publicly available datasets for trachea and kidney segmentation also show significant improvement in segmentation accuracy, demonstrating the generalizability of the method. CONCLUSIONS: Our proposed DABS-MS method can accurately segment the IAC, which can then facilitate the localization of the ANFs. This patient-specific modeling of CI stimulation of the ANFs can help audiologists improve the CI programming, leading to better outcomes for patients with severe-to-profound hearing loss.