A DEEP-LEARNING-BASED METHOD FOR THE LOCALIZATION OF COCHLEAR IMPLANT ELECTRODES IN CT IMAGES

Accurate localization of contacts on cochlear implant (CI) electrode arrays (EAs) in post-implantation CTs (Post-CTs) of Ci recipients is important for assisting audiologists in customizing CI settings. We propose a two-step method to localize CI contacts in Post-CTs when the resolution of the images permits distinguishing individual contacts. Given a Post-CT, we first use conditional generative adversarial networks (cGANs) to generate an image in which voxel values are proportional to the distance to the nearest candidate contact. We refer to this image as the likelihood map. This is followed by a post-processing method applied to the likelihood map to estimate the accurate location of each individual contact. The method has been evaluated on 30 Post-CTs implanted with 17 contacts EAs manufactured by Advanced Bionics Corporation. It localized all contacts in 29 cases and achieved a median localization error of 0.12 mm for the successful cases, which is comparable to what is achieved with a state-of-the-art method that requires sets of carefully designed EA-specific features and parameters.