Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans.

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.

[1]  William E. Lorensen,et al.  Marching cubes: a high resolution 3D surface construction algorithm , 1996 .

[2]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[3]  J. Michael Fitzpatrick,et al.  Customized, rapid-production microstereotactic table for surgical targeting: description of concept and in vitro validation , 2009, International Journal of Computer Assisted Radiology and Surgery.

[4]  Benoit M Dawant,et al.  Automatic Identification and 3D Rendering of Temporal Bone Anatomy , 2009, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.

[5]  Kaleem Siddiqi,et al.  Flux driven automatic centerline extraction , 2005, Medical Image Anal..

[6]  Omid Majdani,et al.  Clinical Validation Study of Percutaneous Cochlear Access Using Patient-Customized Microstereotactic Frames , 2010, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.

[7]  Benoit M Dawant,et al.  Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values. , 2008, Medical physics.

[8]  Omid Majdani,et al.  Automatic determination of optimal linear drilling trajectories for cochlear access accounting for drill‐positioning error , 2010, The international journal of medical robotics + computer assisted surgery : MRCAS.

[9]  Benoit M. Dawant,et al.  An atlas-navigated optimal medial axis and deformable model algorithm (NOMAD) for the segmentation of the optic nerves and chiasm in MR and CT images , 2011, Medical Image Anal..

[10]  R.J. Maciunas,et al.  An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head , 1996, IEEE Transactions on Biomedical Engineering.

[11]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[12]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[13]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Benoit M. Dawant,et al.  The adaptive bases algorithm for intensity-based nonrigid image registration , 2003, IEEE Transactions on Medical Imaging.

[15]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[16]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[17]  R. Balachandran,et al.  Minimally Invasive, Image-Guided, Facial-Recess Approach to the Middle Ear: Demonstration of the Concept of Percutaneous Cochlear Access In Vitro , 2005, Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology.

[18]  R. Martin,et al.  Computer‐assisted paleoanthropology , 1998 .

[19]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[20]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .