Three-dimensional reconstruction of individual cervical vertebrae from cone-beam computed-tomography images.

Introduction: The visualization of cervical vertebral morphology has potential in skeletal age assessment; however, thus far, it has only been described in planar images. The objective of this article is to present a novel segmentation algorithm for automatic 3-dimensional (3D) reconstruction of individual cervical vertebrae from cone-beam computed-tomography (CBCT) volumetric data sets.

Methods: CBCT data sets of 3 subjects representing different skeletal age groups with no potential health influences on cervical anatomy were identified from a larger subject sample. A visualization toolkit was used to demonstrate the surface topologic shape of cervical vertebrae C1 through C3. The cervical vertebrae were segmented by using a custom algorithm based on individual voxel intensity distribution analysis and propagation from a densitometric start point to generate the whole vertebra. The segmentation algorithm was combined with toolkit visualization to render and save the cervical vertebra in 3D space.

Results: The developed segmentation algorithm separated individual cervical vertebrae successfully. It was robust and efficient. Observed 3D cervical vertebral morphologic features from the 3 examples matched the known 2-dimensional sagittal shape changes of the cervical vertebra with respect to subject age and skeletal maturation.

Conclusions: Segmentation of individual vertebrae proved possible from CBCT volumetric data sets. This provides a 3D approach to the biologic aging of orthodontic patients by using images of the cervical spine. It also has potential in studying disease processes such as spinal fractures consequent to osteoporosis.