Validation of active shape model techniques for intra-cochlear anatomy segmentation in CT images

Abstract

Cochlear implants (CIs) have been shown to be highly effective restorative devices for patients suffering from severe-to-profound hearing loss. Hearing outcomes with CIs are dependent on the positions of the electrodes with respect to intracochlear anatomy. However, intra-cochlear anatomy can only be directly visualized using high resolution modalities such as μCT, which cannot be used in vivo. Despite this limitation, we have developed an active shape model approach for segmenting the intra-cochlear anatomy by leveraging the visible structures as landmarks. Still, due to the limited availability of μCT specimens, the segmentation method was validated on only a small dataset of 5 samples. In this study, we expand the dataset to 16 samples and provide a more comprehensive validation of the method’s performance with respect to model parameters and training set size. We found parameters that optimize mean surface segmentation performance to 0.11mm. Parameters that corresponded to tighter constraints generally led to smaller errors, and returns on segmentation performance begin diminishing after 11 samples, thus suggesting that the main performance bottleneck is due to the searching scheme rather than a limited training set size. These results are critical to understand the limitations of the method for clinical use and for future development.