Two-level Training of a 3d U-Net for Accurate Segmentation of the Intra-cochlear Anatomy in Head CTs with Limited Ground Truth Training Data

摘要

Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to treat patients with hearing loss.For CI recipients, sound bypasses the natural transduction mechanism and directly stimulates the neural regions, thuscreating a sense of hearing. Post-operatively, CIs need to be programmed. Traditionally, this is done by an audiologist whois blind to the positions of the electrodes relative to the cochlea and only relies on the subjective response of the patient.Multiple programming sessions are usually needed, which can take a frustratingly long time. We have developed an imageguidedcochlear implant programming (IGCIP) system to facilitate the process. In IGCIP, we segment the intra-cochlearanatomy and localize the electrode arrays in the patient’s head CT image. By utilizing their spatial relationship, we cansuggest programming settings that can significantly improve hearing outcomes. To segment the intra-cochlear anatomy,we use an active shape model (ASM)-based method. Though it produces satisfactory results in most cases, sub-optimalsegmentation still happens. As an alternative, herein we explore using a deep learning method to perform the segmentationtask. Large image sets with accurate ground truth (in our case manual delineation) are typically needed to train a deeplearning model for segmentation but such a dataset does not exist for our application. To tackle this problem, we usesegmentations generated by the ASM-based method to pre-train the model and fine-tune it on a small image set for whichaccurate manual delineation is available. Using this method, we achieve better results than the ASM-based method.