title: Facial nerve image enhancement from CBCT using supervised learning technique.
abstract: Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.
authors.lastname: Ping Lu
authors.forename: Livia
authors.initials: L
authors.lastname: Barazzetti
authors.forename: Vimal
authors.initials: V
authors.lastname: Chandran
authors.forename: Kate
authors.initials: K
authors.lastname: Gavaghan
authors.forename: Stefan
authors.initials: S
authors.lastname: Weber
authors.forename: Nicolas
authors.initials: N
authors.lastname: Gerber
authors.forename: Mauricio
authors.initials: M
authors.lastname: Reyes
mesh_terms: D019929:Cochlear Implantation; D054893:Cone-Beam Computed Tomography; D005154:Facial Nerve; D007089:Image Enhancement; D000069553:Supervised Machine Learning