Cochlear implants (CIs) use a surgically implanted electrode array to treat severe-to-profound sensorineural hearing loss. Audiologists program CIs by selecting a number of stimulation parameters for the CI processor to optimize hearing performance. It has been shown in previous research that audiologists arrive at CI settings that lead to a better hearing outcome when they are provided an estimate of which regions of the auditory nerve are being activated by each electrode for a patient. If the neural fibers could be localized, neural fiber models could be used to estimate activa tion in response to electrode activation for individual patients. However, the neural fibers are so small they are not visible in clinical images. In this project, our aim is to develop an active-shape model based solution to automatically localize the Internal Auditory Canal (IAC), which houses the auditory nerves and has borders that are visible in CT scans, to serve as a landmark for localizing the nerve fibers . Seven manually segmented IAC volumes were used to create and validate our method using a leave-one-out approach. We found that the mean surface errors of the dataset ranged from ~0.4 to ~1.2 CT voxels (0.13 mm to 0.37 mm). These results suggest that our IAC segmentation is highly accurate and could provide an excellent landmark for estimating fiber position.