Super-resolution segmentation network for inner-ear tissue segmentation

Cochlear implants (CIs) are considered the standard-of-care treatment for profound sensory-based hearing loss. Several groups have proposed computational models of the cochlea in order to study the neural activation patterns in response to CI stimulation. However, most of the current implementations either rely on high-resolution histological images that cannot be customized for CI users or CT images that lack the spatial resolution to show cochlear structures. In this work, we propose to use a deep learning-based method to obtain μCT level tissue labels using patient CT images. Experiments showed that the proposed super-resolution segmentation architecture achieved very good performance on the inner-ear tissue segmentation. Our best-performing model (0.871) outperformed the UNet (0.746), VNet (0.853), nnUNet (0.861), TransUNet (0.848), and SRGAN (0.780) in terms of mean dice score.