2018
DOI: 10.1002/mp.13185
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Automatic localization of closely spaced cochlear implant electrode arrays in clinical CTs

Abstract: Our validation study shows that our method can localize closely spaced CI arrays with an accuracy close to what is achievable by an expert on clinical CTs. This represents a crucial step toward automating IGCIP and translating it from the laboratory to the clinical workflow.

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Cited by 25 publications
(21 citation statements)
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“…Post-operative CT scan and image analysis [29][30][31][32][33][34] Med-El users (all both straight).…”
Section: Methodsmentioning
confidence: 99%
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“…Post-operative CT scan and image analysis [29][30][31][32][33][34] Med-El users (all both straight).…”
Section: Methodsmentioning
confidence: 99%
“…To determine the intra-cochlear location of the electrodes, patient-specific cochlea shape was derived from the patient CT scans using a model that includes segmentation of the intra-cochlear anatomy [29][30][31][32][33][34] . This analysis quantifies the estimated electrode position in three dimensions.…”
Section: Methodsmentioning
confidence: 99%
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“…Twenty-one post-lingually deafened adult CI recipients participated in this study. Electrode placement, including scalar location, average electrode-to-modiolus distance (trueM¯), and angular insertion depth were determined by CT imaging using validated CI position analysis algorithms (e.g., Noble et al, 2013; Zhao et al, 2018, 2019). CI programs were created using 4–22 electrodes with equal spatial distribution of active electrodes across the array.…”
Section: Methodsmentioning
confidence: 99%
“…All experimental activities were completed in accordance with IRB approved protocols at the Vanderbilt University and the Vanderbilt University Medical Center. Electrode placement, including scalar location, mean and variance of electrode-to-modiolus distance, and angular insertion depth were all determined by CT imaging using validated CI position analysis algorithms (Noble et al, 2013; Zhao et al, 2018, 2019). These algorithms were created using a statistical shape model of 10 cadaver temporal bone microCT images.…”
Section: Methodsmentioning
confidence: 99%