ORCID
https://orcid.org/0000-0001-9712-3119
Date of Award
Summer 8-23-2021
Degree Name
Master of Science (MS)
Degree Type
Thesis
Abstract
There is a steep learning curve for surgeons performing cochlear implant surgeries. We aimed to use computer vision to track anatomical features with the goal of helping surgeons perform cochlear implant surgery without damaging the cochlea. We compared nine algorithms in total, seven object tracking algorithms and two optical flow algorithms utilizing the LucasKanade method, on manually created cochlear implant surgery videos to determine the accuracy associated with each. Compared with eight other algorithms, we observed that an iterative pyramidal implementation of the Lucas-Kanade (IPLK) method, implemented through OpenCV, performed the best. The IPLK method had the lowest error rate on five out of the six videos, with zero error on four. In conclusion, the IPLK method is the most accurate at tracking the locations of the anatomical structures in a video of a cochlear implant surgery. Computer vision may be a novel and valuable tool to improve surgical results.
Language
English (en)
Chair
Jonathan Silva
Committee Members
Tao Ju Neal Patwari Gustavo Malkomes Dennis Barbour