Cochlear implants (CIs) are considered standard treatment for patients who experience sensory-based hearing loss. During the surgery, an electrode array will be inserted into the cochlea to directly stimulate auditory nerve fibers (ANFs). Although CI devices have been remarkably successful at restoring audibility, the neural interface is unknown to audiologists, and patients need to experience programming sessions that are frustratingly long and usually lead to suboptimal results. Our group developed a high-resolution computational model in order to simulate the neural response triggered by CIs. However, the semi-automatic ANF segmentation approach we used for that model relies heavily on manual adjustment, and the central axons of those ANFs may still pass through the bone mistakenly due to the limitation of only one set of landmarks in the modiolus. In this work, we introduced a fully automatic ANF segmentation method. The peripheral and central axon of an ANF will be estimated individually based on five sets of automatically generated landmarks. The fast marching method is used to find the geodesic paths for the peripheral axons between the surfaces of the scala tympani (ST) and scala vestibuli (SV) meshes. Cylindrical coordinate systems are constructed based on landmarks and are used to smoothly interpolate trajectories for the spiral central axons. Experiments show that our proposed method outperforms the original method and achieves impressive performance with 0 overlapping ANFs and 0 ANFs passing through the bone. The number of ANFs that pass through ST or SV is also reduced by 36.1%.
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