Auditory nerve fiber segmentation methods for neural activation modeling

Ahmet Cakir; Robert F. Labadie; Jack H. Noble

doi:10.1117/12.2513006

8 March 2019 Auditory nerve fiber segmentation methods for neural activation modeling

Ahmet Cakir, Robert F. Labadie, Jack H. Noble

Proceedings Volume 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling; 109511K (2019) https://doi.org/10.1117/12.2513006
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Cochlear implants (CIs) are considered the standard-of-care treatment for severe-to-profound, sensorineural hearing loss. The positioning of the array within the cochlea affects which auditory nerve fibers are stimulated by which electrode and is known to affect hearing outcomes. Image-Guided CI Programming (IGCIP) techniques, where estimates of the position of the electrodes relative to the nerve fibers are provided to the programming audiologist, have been shown to lead to significantly improved hearing outcomes. With the current IGCIP approach, assumptions are made about electrical current spread to estimate which fiber groups are activated based on their distance to the electrode. To improve our estimates, we are developing an approach for creating patient-customized, high-resolution, electro-anatomical models of the electrically stimulated cochlea coupled with computational auditory nerve fiber models (ANFMs) to permit physics-based estimation of neural stimulation patterns. In this paper, our goal is to evaluate semi- and fully-automatic techniques for segmenting auditory nerve fibers that will be used in creating ANFMs, as well as to quantify the effect of change in fiber location on the neural activation patterns. Our semi-automatic approach uses path finding algorithms to connect automatically estimated landmarks, and our automatic approach is atlas-based. We found that repeatability in fiber localization with semi-automatic segmentation is 0.1 mm on average and results in modeled activation patterns that have 83% overlap. The difference between the semi-automatic and automatic segmentations led to higher average differences of 0.19 mm and lower activation pattern overlap of 74%.

Conference Presentation

Citation Download Citation

Ahmet Cakir, Robert F. Labadie, and Jack H. Noble "Auditory nerve fiber segmentation methods for neural activation modeling", Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511K (8 March 2019); https://doi.org/10.1117/12.2513006

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