2 research outputs found
Validation of image-guided cochlear implant programming techniques
Cochlear implants (CIs) are a standard treatment for patients who experience
severe to profound hearing loss. Recent studies have shown that hearing outcome
is correlated with intra-cochlear anatomy and electrode placement. Our group
has developed image-guided CI programming (IGCIP) techniques that use image
analysis methods to both segment the inner ear structures in pre- or
post-implantation CT images and localize the CI electrodes in post-implantation
CT images. This permits to assist audiologists with CI programming by
suggesting which among the contacts should be deactivated to reduce electrode
interaction that is known to affect outcomes. Clinical studies have shown that
IGCIP can improve hearing outcomes for CI recipients. However, the sensitivity
of IGCIP with respect to the accuracy of the two major steps: electrode
localization and intra-cochlear anatomy segmentation, is unknown. In this
article, we create a ground truth dataset with conventional CT and micro-CT
images of 35 temporal bone specimens to both rigorously characterize the
accuracy of these two steps and assess how inaccuracies in these steps affect
the overall results. Our study results show that when clinical pre- and
post-implantation CTs are available, IGCIP produces results that are comparable
to those obtained with the corresponding ground truth in 86.7% of the subjects
tested. When only post-implantation CTs are available, this number is 83.3%.
These results suggest that our current method is robust to errors in
segmentation and localization but also that it can be improved upon.
Keywords: cochlear implant, ground truth, segmentation, validationComment: 37 pages, 12 figures, 7 table
Automatic techniques for cochlear implant CT image analysis
The goals of this dissertation are to fully automate the image processing
techniques needed in the post-operative stage of IGCIP and to perform a
thorough analysis of (a) the robustness of the automatic image processing
techniques used in IGCIP and (b) assess the sensitivity of the IGCIP process as
a whole to individual components. The automatic methods that have been
developed include the automatic localization of both closely- and
distantly-spaced CI electrode arrays in post-implantation CTs and the automatic
selection of electrode configurations based on the stimulation patterns.
Together with the existing automatic techniques developed for IGCIP, the
proposed automatic methods enable an end-to-end IGCIP process that takes pre-
and post-implantation CT images as input and produces a patient-customized
electrode configuration as output.Comment: This is a preprint of Yiyuan Zhao's Ph.D. dissertation from
Vanderbilt University, Nashville, TN, USA. Trivial formatting modifications
have been made in the arxiv version for readability. Vanderbilt University
Electronic These & Dissertation (https://etd.library.vanderbilt.edu/) has the
original submission on May 11 2018, and will be released on May 11 202