Automatic Classification of Cochlear Implant Electrode Cavity Positioning

Abstract

Cochlear Implants (CIs) restore hearing using an electrode array that is surgically implanted into the intra-cochlear cavities. Research has indicated that each electrode can lie in one of several cavities and that location is significantly associated with hearing outcomes. However, comprehensive analysis of this phenomenon has not been possible because the cavities are not directly visible in clinical CT images and because existing methods to estimate cavity location are not accurate enough, labor intensive, or their accuracy has not been validated. In this work, a novel graph-based search is presented to automatically identify the cavity in which each electrode is located. We test our approach on CT scans from a set of 34 implanted temporal bone specimens. High resolution µCT scans of the specimens, where cavities are visible, show our method to have 98% cavity classification accuracy. These results indicate that our methods could be used on a large scale to study the link between electrode placement and outcome, which could lead to advances that improve hearing outcomes for CI users.