Atlas-based automatic internal auditory canal localization with a weakly-supervised 3D U-Net

Hannah G. Mason; Jack H. Noble

doi:10.1117/12.2655625

3 April 2023 Atlas-based automatic internal auditory canal localization with a weakly-supervised 3D U-Net

Hannah G. Mason, Jack H. Noble

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 1246627 (2023) https://doi.org/10.1117/12.2655625
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Cochlear Implants (CIs) are neural prosthetics which use an array of implanted electrodes to improve hearing in patients with severe-to-profound hearing loss. After implantation, the CI is programmed by audiologists who adjust various parameters to optimize hearing performance for the patient. Without knowing which Auditory Nerve Fibers (ANFs) are being stimulated by each electrode, this process can require dozens of programming sessions and often does not lead to optimal programming. The Internal Auditory Canal (IAC) houses the ANFs as they travel from the implantation site, the cochlea, to the brain. In this paper, we present a method for localizing the IAC in a CT image by deforming an atlas IAC mesh to a CT image using a 3D U-Net. Our results suggest this method is more accurate than an active shape model-based method when tested on a test set of 20 images with ground truth. This IAC segmentation can be used to infer the position of the invisible ANFs to assist with patient-specific CI programming.

Citation Download Citation

Hannah G. Mason and Jack H. Noble "Atlas-based automatic internal auditory canal localization with a weakly-supervised 3D U-Net", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 1246627 (3 April 2023); https://doi.org/10.1117/12.2655625

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