Segmentation of the facial nerve using a weakly supervised GAN

Minh Vu; Yubo Fan; Jack H. Noble

doi:10.1117/12.3008656

29 March 2024 Segmentation of the facial nerve using a weakly supervised GAN

Minh Vu, Yubo Fan, Jack H. Noble

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 1292818 (2024) https://doi.org/10.1117/12.3008656
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Cochlear implants (CI) are a highly successful neural-prosthetic device, recreating the sensation of hearing by directly stimulating the nerve fibers inside the cochlea for individuals experiencing severe to profound hearing loss. Implantation traditionally requires invasive procedures such as mastoidectomy, however minimally invasive techniques such as percutaneous cochlear access have also been investigated. This method involves drilling a single hole through the skull surface, granting direct access to the cochlea where the CI can be threaded. The trajectory of this insertion typically involves traversing the facial recess, a region approximately 1.0–3.5 mm in width bounded posteriorly by the facial nerve and anteriorly by the chorda tympani The determination of a safe drilling trajectory is highly crucial, as damage to these structures during surgery may result in a loss of taste (chorda) or facial paralysis (facial nerve). It is therefore very important that these clinical structures are segmented accurately for the drilling trajectory planning process. In this work, we propose the use of a conditional generative adversarial network (cGAN) to automatically segment the facial nerve. Our method can also make up for noisy and disconnected generated segmentations using a minimum cost path search function between the endpoints. Our network utilized weakly supervised approach, being trained on a small sample of 12 manually segmented image and supplemented with 120 automatically segmented image created through atlas-based image registration. Our method generated segmentations with an average mean surface error of only 0.24mm, reducing the mean error of the original method by ~50%.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minh Vu, Yubo Fan, and Jack H. Noble "Segmentation of the facial nerve using a weakly supervised GAN", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 1292818 (29 March 2024); https://doi.org/10.1117/12.3008656

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