Automatic localization of closely spaced cochlear implant electrode arrays in clinical CTs

Cochlear implants (CIs) are neural prosthetic devices that provide a sense of sound to people who experience profound hearing loss. Recent research has indicated that there is a significant correlation between hearing outcomes and the intracochlear locations of the electrodes. We have developed an image-guided cochlear implant programming (IGCIP) system based on this correlation to assist audiologists with programming CI devices. One crucial step in our IGCIP system is the localization of CI electrodes in postimplantation CTs. Existing methods for this step are either not fully automated or not robust. When the CI electrodes are closely spaced, it is more difficult to identify individual electrodes because there is no intensity contrast between them in a clinical CT. The goal of this work is to automatically segment the closely spaced CI electrode arrays in postimplantation clinical CTs.

The proposed method involves firstly identifying a bounding box that contains the cochlea by using a reference CT. Then, the intensity image and the vesselness response of the VOI are used to segment the regions of interest (ROIs) that may contain the electrode arrays. For each ROI, we apply a voxel thinning method to generate the medial axis line. We exhaustively search through all the possible connections of medial axis lines. For each possible connection, we define CI array centerline candidates by selecting two points on the connected medial axis lines as the array endpoints. For each CI array centerline candidate, we use a cost function to evaluate its quality, and the one with the lowest cost is selected as the array centerline. Then, we fit an a priori known geometric model of the array to the centerline to localize the individual electrodes. The method was trained on 28 clinical CTs of CI recipients implanted with three models of closely spaced CI arrays. The localization results are compared with the ground truth localization results manually generated by an expert.

A validation study was conducted on 129 clinical CTs of CI recipients implanted with three models of closely spaced arrays. Ninety-eight percent of the localization results generated by the proposed method had maximum localization errors lower than one voxel diagonal of the CTs. The mean localization error was 0.13 mm, which was close to the rater's consistency error (0.11 mm). The method also outperformed the existing automatic electrode localization methods in our validation study.

Our validation study shows that our method can localize closely spaced CI arrays with an accuracy close to what is achievable by an expert on clinical CTs. This represents a crucial step toward automating IGCIP and translating it from the laboratory to the clinical workflow.