Automatic internal auditory canal segmentation using a weakly supervised 3D U-Net

Hannah G. Mason; Jack H. Noble

doi:10.1117/12.2611897

4 April 2022 Automatic internal auditory canal segmentation using a weakly supervised 3D U-Net

Hannah G. Mason, Jack H. Noble

Author Affiliations +

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120341X (2022) https://doi.org/10.1117/12.2611897
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Cochlear implants (CIs) are neural prosthetics used to improve hearing in patients with severe-to-profound hearing loss. After implantation, the process of fine-tuning the implant for a specific patient is expedited if the audiologist has tools to approximate which auditory nerve fiber regions are being stimulated by the implant’s electrode array. Auditory nerves travel from the cochlea where the prosthetic is implanted to the brain via the internal auditory canal (IAC). In this paper, we present a method for segmenting the IAC in a CT image using weakly supervised 3D UNets. Our approach is to train a U-Net with a custom loss function to refine a localization provided by a previously proposed active-shape-model-based IAC segmentation method. Preliminary results indicate that our proposed approach is successful in refining IAC localization, which is an important step towards accurate auditory nerve fiber localization for neural activation modeling.

Citation Download Citation

Hannah G. Mason and Jack H. Noble "Automatic internal auditory canal segmentation using a weakly supervised 3D U-Net", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120341X (4 April 2022); https://doi.org/10.1117/12.2611897

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