Title: Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans
Abstract: Medical PhysicsVolume 38, Issue 10 p. 5590-5600 Radiation imaging physics Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans Fitsum A. Reda, Fitsum A. Reda Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37235Search for more papers by this authorJack H. Noble, Jack H. Noble Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37235Search for more papers by this authorAlejandro Rivas, Alejandro Rivas Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232Search for more papers by this authorTheodore R. McRackan, Theodore R. McRackan Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232Search for more papers by this authorRobert F. Labadie, Robert F. Labadie Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232Search for more papers by this authorBenoit M. Dawant, Benoit M. Dawant Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37235 Author to whom correspondence should be addressed. Electronic mail: [email protected].Search for more papers by this author Fitsum A. Reda, Fitsum A. Reda Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37235Search for more papers by this authorJack H. Noble, Jack H. Noble Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37235Search for more papers by this authorAlejandro Rivas, Alejandro Rivas Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232Search for more papers by this authorTheodore R. McRackan, Theodore R. McRackan Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232Search for more papers by this authorRobert F. Labadie, Robert F. Labadie Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee 37232Search for more papers by this authorBenoit M. Dawant, Benoit M. Dawant Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee 37235 Author to whom correspondence should be addressed. Electronic mail: [email protected].Search for more papers by this author First published: 22 September 2011 https://doi.org/10.1118/1.3634048Citations: 19 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract Purpose: Cochlear implant surgery is used to implant an electrode array in the cochlea to treat hearing loss. The authors recently introduced a minimally invasive image-guided technique termed percutaneous cochlear implantation. This approach achieves access to the cochlea by drilling a single linear channel from the outer skull into the cochlea via the facial recess, a region bounded by the facial nerve and chorda tympani. To exploit existing methods for computing automatically safe drilling trajectories, the facial nerve and chorda tympani need to be segmented. The goal of this work is to automatically segment the facial nerve and chorda tympani in pediatric CT scans. Methods: The authors have proposed an automatic technique to achieve the segmentation task in adult patients that relies on statistical models of the structures. These models contain intensity and shape information along the central axes of both structures. In this work, the authors attempted to use the same method to segment the structures in pediatric scans. However, the authors learned that substantial differences exist between the anatomy of children and that of adults, which led to poor segmentation results when an adult model is used to segment a pediatric volume. Therefore, the authors built a new model for pediatric cases and used it to segment pediatric scans. Once this new model was built, the authors employed the same segmentation method used for adults with algorithm parameters that were optimized for pediatric anatomy. Results: A validation experiment was conducted on 10 CT scans in which manually segmented structures were compared to automatically segmented structures. The mean, standard deviation, median, and maximum segmentation errors were 0.23, 0.17, 0.18, and 1.27 mm, respectively. Conclusions: The results indicate that accurate segmentation of the facial nerve and chorda tympani in pediatric scans is achievable, thus suggesting that safe drilling trajectories can also be computed automatically. 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