Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values

• Published in: Medical physics (Lancaster) 2008-12, Vol.35 (12), p.5375-5384
• Main Authors: Noble, Jack H., Warren, Frank M., Labadie, Robert F., Dawant, Benoit M.
• Format: Article
• Language: English
• Subjects: Algorithms; Anatomy; atlas‐based segmentation; Automatic Data Processing; Automation; chorda tympani; Chorda Tympani Nerve - pathology; Cochlea - diagnostic imaging; Cochlea - surgery; cochlear implant; Cochlear Implantation - instrumentation; Cochlear Implantation - methods; Cochlear Implants; Computed tomography; computerised tomography; ear; Ears; Electrodes; Equipment Design; facial nerve; Facial Nerve - pathology; Humans; Image Processing, Computer-Assisted; Image registration; Image scanners; image segmentation; Imaging, Three-Dimensional; Medical image contrast; Medical image segmentation; Medical imaging; Models, Statistical; model‐based segmentation; Neural prosthetics; neurophysiology; optimal path; prosthetics; Radiation Imaging Physics; Segmentation; Tomography, X-Ray Computed - methods
• ISSN: 0094-2405; 2473-4209; 0094-2405
• DOI: 10.1118/1.3005479

In cochlear implant surgery, an electrode array is permanently implanted in the cochlea to stimulate the auditory nerve and allow deaf people to hear. A minimally invasive surgical technique has recently been proposed—percutaneous cochlear access—in which a single hole is drilled from the skull surface to the cochlea. For the method to be feasible, a safe and effective drilling trajectory must be determined using a preoperative CT. Segmentation of the structures of the ear would improve trajectory planning safety and efficiency and enable the possibility of automated planning. Two important structures of the ear, the facial nerve and the chorda tympani, are difficult to segment with traditional methods because of their size (diameters as small as 1.0 and 0.3 mm , respectively), the lack of contrast with adjacent structures, and large interpatient variations. A multipart, model-based segmentation algorithm is presented in this article that accomplishes automatic segmentation of the facial nerve and chorda tympani. Segmentation results are presented for ten test ears and are compared to manually segmented surfaces. The results show that the maximum error in structure wall localization is ∼ 2 voxels for the facial nerve and the chorda, demonstrating that the method the authors propose is robust and accurate.