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.
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.
Authors: Robert F Labadie; Ramya Balachandran; Jason E Mitchell; Jack H Noble; Omid Majdani; David S Haynes; Marc L Bennett; Benoit M Dawant; J Michael Fitzpatrick Journal: Otol Neurotol Date: 2010-01 Impact factor: 2.311
Authors: Robert F Labadie; Jason Mitchell; Ramya Balachandran; J Michael Fitzpatrick Journal: Int J Comput Assist Radiol Surg Date: 2009-02-28 Impact factor: 2.924
Authors: Theodore R McRackan; Matthew L Carlson; Fitsum A Reda; Jack H Noble; Alejandro Rivas Journal: Otol Neurotol Date: 2014-06 Impact factor: 2.311
Authors: Theodore R McRackan; Fitsum A Reda; Alejandro Rivas; Jack H Noble; Mary S Dietrich; Benoit M Dawant; Robert F Labadie Journal: Otol Neurotol Date: 2012-04 Impact factor: 2.311
Authors: Ramya Balachandran; Fitsum A Reda; Jack H Noble; Grégoire S Blachon; Benoit M Dawant; J Michael Fitzpatrick; Robert F Labadie Journal: Otolaryngol Head Neck Surg Date: 2014-01-21 Impact factor: 3.497
Authors: Bradley M Gare; Thomas Hudson; Seyed A Rohani; Daniel G Allen; Sumit K Agrawal; Hanif M Ladak Journal: Int J Comput Assist Radiol Surg Date: 2019-11-23 Impact factor: 2.924
Authors: Wilhelm Wimmer; Nicolas Gerber; Jérémie Guignard; Patrick Dubach; Martin Kompis; Stefan Weber; Marco Caversaccio Journal: Eur Arch Otorhinolaryngol Date: 2014-03-14 Impact factor: 2.503