Identification of vertebral deformations in two dimensions (2D) is a challenging task due to the projective nature
of radiographic images and natural anatomical variability of vertebrae. By generating detailed three-dimensional
(3D) anatomical images, computed tomography (CT) enables accurate measurement of vertebral deformations.
We present a novel approach to quantitative vertebral morphometry (QVM) based on parametric modeling
of the vertebral body shape in 3D. A detailed 3D representation of the vertebral body shape is obtained by
automatically aligning a parametric 3D model to vertebral bodies in CT images. The parameters of the 3D
model describe clinically meaningful morphometric vertebral body features, and QVM in 3D is performed by
comparing the parameters to their statistical values. By applying statistical classification analysis, thresholds
and parameters that best discriminate between normal and fractured vertebral bodies are determined. The
proposed QVM in 3D was applied to 454 normal and 228 fractured vertebral bodies, yielding classification
sensitivity of 92:5% at 7:5% specificity, with corresponding accuracy of 92:5% and precision of 86:1%. The 3D
shape parameters that provided the best separation between normal and fractured vertebral bodies were the
vertebral body height, and the inclination and concavity of both vertebral endplates. The described QVM in
3D is able to efficiently discriminate between normal and fractured vertebral bodies, and identify morphological
cases (wedge, (bi)concavity, crush) and grades (1, 2, 3) of vertebral body deformations. It may be therefore
valuable for diagnosing and predicting vertebral fractures in patients who are at risk of osteoporosis.