title: Segmentation of Trabecular Jaw Bone on Cone Beam CT Datasets.
abstract: BACKGROUND
  The term bone quality is often used in a dentomaxillofacial context, for example in implant planning, as bone density and bone structure have been linked to primary implant success.
  
  
  OBJECTIVE
  This research aimed to investigate the performance of adaptive thresholding of trabecular bone in cone beam CT (CBCT) images. The segmentation quality was assessed for different imaging devices and upper and lower jaws.
  
  
  METHODS
  Four jaws were scanned with eight CBCT scanners and one micro-CT device. Images of the jaws were spatially aligned with the micro-CT images. Two volumes of interest for each jaw were manually delineated. Trabecular bone in the volumes of interest in the micro-CT images was segmented so that the micro-CT images could serve as high-resolution ground truth images. The volumes of interest in the CBCT images were segmented using both global and adaptive thresholding.
  
  
  RESULTS
  Segmentation was significantly better for the lower jaw than for the upper jaw. Differences in performance between the scanners were significant for both jaws. Adaptive thresholding performed significantly better in segmenting the bone structure out of CBCT images.
  
  
  CONCLUSIONS
  When assessing jaw bone structure, the observer should always choose adaptive thresholding. It remains a challenge to identify the optimal threshold selection for the structural assessment of jaw bone.
authors.affiliation: OMFS-IMPATH Research Group, Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.
authors.forename: Olivia
authors.initials: O
authors.lastname: Nackaerts
authors.affiliation: Medical Image Computing, Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium.
authors.forename: Maarten
authors.initials: M
authors.lastname: Depypere
authors.affiliation: Leuven University Centre for Medical Physics in Radiology, University Hospitals Leuven, Leuven, Belgium.
authors.forename: Guozhi
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Department of Oral Health Sciences, KU Leuven, Leuven, Belgium.
authors.forename: Bart
authors.initials: B
authors.lastname: Vandenberghe
authors.affiliation: Medical Image Computing, Center for Processing Speech and Images, Department of Electrical Engineering & iMinds, KU Leuven, Leuven, Belgium.
authors.forename: Frederik
authors.identifier: http://orcid.org/0000-0003-0027-1479
authors.initials: F
authors.lastname: Maes
authors.affiliation: OMFS-IMPATH Research Group, Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.
authors.forename: Reinhilde
authors.initials: R
authors.lastname: Jacobs
mesh_terms: D015519:Bone Density; D054893:Cone-Beam Computed Tomography / Q000295:instrumentation*; D006801:Humans; D066298:In Vitro Techniques; D008334:Mandible / Q000000981:diagnostic imaging*; D008437:Maxilla / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted; D055114:X-Ray Microtomography / Q000295:instrumentation*
keywords: CBCT imaging; bone density; micro-CT