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Analysis of middle ear morphology for design of a transnasal endoscope
Author(s): Minh Vu; Rueben A. Banalagay; Dongqing Zhang; Alejandro Rivas; Loris Fichera; Robert Webster; Robert F. Labadie; Jack H. Noble
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Paper Abstract

Cholesteatomas are benign lesions that form in the middle ear (ME). They can cause debilitating side effects including hearing loss, recurrent ear infection and drainage, and balance disruption. The current approach for positively identifying cholesteatomas requires intraoperative visualization either by lifting the ear drum or transmitting an endoscope through the ear canal and tympanic membrane – procedures which are typically done in and operating room with the patient under general anesthesia. We are developing a novel endoscope that can be inserted trans-nasally and could potentially be used in an outpatient setting allowing clinicians to easily detect and visualize cholesteatomas and other middle ear conditions. A crucial part of designing this device is determining the degrees of freedom necessary to visualize the regions of interest in the middle ear space. To permit virtual evaluation of scope design, in this work we propose to create a library of models of the most difficult to visualize region of the middle ear, the retrotympanum (RT), which is located deep and posterior to the tympanic membrane. We have designed a semi-automated atlas-based approach for segmentation of the RT. Our approach required 2-3 minutes of manual interaction for each of 20 cases tested. Each result was verified to be accurate by an experienced otologist. These results show the method is efficient and accurate enough to be applied to a large scale dataset. We also created a statistical shape model from the resulting segmentations that can be used to synthesize new plausible RT shapes for comprehensive virtual evaluation of endoscope designs and show that it can represent new RT shapes with average errors of 0.5 mm.

Paper Details

Date Published: 8 March 2019
PDF: 6 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512S (8 March 2019); doi: 10.1117/12.2513841
Show Author Affiliations
Minh Vu, Vanderbilt Univ. (United States)
Rueben A. Banalagay, Vanderbilt Univ. (United States)
Dongqing Zhang, Vanderbilt Univ. (United States)
Alejandro Rivas, Vanderbilt Univ. Medical Ctr. (United States)
Loris Fichera, Worcester Polytechnic Institute (United States)
Robert Webster, Vanderbilt Univ. (United States)
Robert F. Labadie, Vanderbilt Univ. Medical Ctr. (United States)
Jack H. Noble, Vanderbilt Univ. (United States)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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