Automatic localization of cochlear implant electrodes in CTs with a limited intensity range

Yiyuan Zhao; Benoit M. Dawant; Jack H. Noble

doi:10.1117/12.2254569

24 February 2017 Automatic localization of cochlear implant electrodes in CTs with a limited intensity range

Yiyuan Zhao, Benoit M. Dawant, Jack H. Noble

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101330T (2017) https://doi.org/10.1117/12.2254569
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Cochlear implants (CIs) are neural prosthetics for treating severe-to-profound hearing loss. Our group has developed an image-guided cochlear implant programming (IGCIP) system that uses image analysis techniques to recommend patientspecific CI processor settings to improve hearing outcomes. One crucial step in IGCIP is the localization of CI electrodes in post-implantation CTs. Manual localization of electrodes requires time and expertise. To automate this process, our group has proposed automatic techniques that have been validated on CTs acquired with scanners that produce images with an extended range of intensity values. However, there are many clinical CTs acquired with a limited intensity range. This limitation complicates the electrode localization process. In this work, we present a pre-processing step for CTs with a limited intensity range and extend the methods we proposed for full intensity range CTs to localize CI electrodes in CTs with limited intensity range. We evaluate our method on CTs of 20 subjects implanted with CI arrays produced by different manufacturers. Our method achieves a mean localization error of 0.21mm. This indicates our method is robust for automatic localization of CI electrodes in different types of CTs, which represents a crucial step for translating IGCIP from research laboratory to clinical use.

Conference Presentation

Citation Download Citation

Yiyuan Zhao, Benoit M. Dawant, and Jack H. Noble "Automatic localization of cochlear implant electrodes in CTs with a limited intensity range", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101330T (24 February 2017); https://doi.org/10.1117/12.2254569

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