Statistical shape model segmentation and frequency mapping of cochlear implant stimulation targets in CT

Abstract

Cochlear implant (CI) surgery is considered standard of care treatment for severe hearing loss. CIs are currently programmed using a one-sizefits- all type approach. Individualized, position-based CI programming schemes have the potential to significantly improve hearing outcomes. This has not been possible because the position of stimulation targets is unknown due to their small size and lack of contrast in CT. In this work, we present a segmentation approach that relies on a weighted active shape model created using microCT scans of the cochlea acquired ex-vivo in which stimulation targets are visible. The model is fitted to the partial information available in the conventional CTs and used to estimate the position of structures not visible in these images. Quantitative evaluation of our method results in Dice scores averaging 0.77 and average surface errors of 0.15 mm. These results suggest that our approach can be used for position-dependent image-guided CI programming methods.