Automatic measurement of vertebral body deformations in CT images based on a 3D parametric model

Accurate and objective evaluation of vertebral body deformations represents an important part of the clinical diagnostics and therapy of pathological conditions affecting the spine. Although modern clinical practice is oriented towards threedimensional (3D) imaging techniques, the established methods for the evaluation of vertebral body deformations are based on measurements in two-dimensional (2D) X-ray images. In this paper, we propose a method for automatic measurement of vertebral body deformations in computed tomography (CT) images that is based on efficient modeling of the vertebral body shape with a 3D parametric model. By fitting the 3D model to the vertebral body in the image, quantitative description of normal and pathological vertebral bodies is obtained from the value of 25 parameters of the model. The evaluation of vertebral body deformations is based on the distance of the observed vertebral body from the distribution of the parameter values of normal vertebral bodies in the parametric space. The distribution is obtained from 80 normal vertebral bodies in the training data set and verified with eight normal vertebral bodies in the control data set. The statistically meaningful distance of eight pathological vertebral bodies in the study data set from the distribution of normal vertebral bodies in the parametric space shows that the parameters can be used to successfully model vertebral body deformations in 3D. The proposed method may therefore be used to assess vertebral body deformations in 3D or provide clinically meaningful observations that are not available when using 2D methods that are established in clinical practice.

[1]  Dongsung Kim,et al.  A fully automatic vertebra segmentation method using 3D deformable fences , 2009, Comput. Medical Imaging Graph..

[2]  Gali Dar,et al.  Vertebral body shape variation in the thoracic and lumbar spine: Characterization of its asymmetry and wedging , 2008, Clinical anatomy.

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  R. Ziegler,et al.  A newly developed spine deformity index (SDI) to quantitate vertebral crush fractures in patients with osteoporosis. , 1988, Bone and mineral.

[5]  Robert Epstein,et al.  Comparison of methods for defining prevalent vertebral deformities: The study of osteoporotic fractures , 1995, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[6]  F. Pernus,et al.  Automated detection of spinal centrelines, vertebral bodies and intervertebral discs in CT and MR images of lumbar spine , 2010, Physics in medicine and biology.

[7]  R. Eastell,et al.  Comparison of methods for the visual identification of prevalent vertebral fracture in osteoporosis , 2004, Osteoporosis International.

[8]  Vipin Chaudhary,et al.  Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis , 2011, Medical Imaging.

[9]  André Mastmeyer,et al.  A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine , 2006, Medical Image Anal..

[10]  Paul M. Parizel,et al.  Spinal Imaging: Diagnostic Imaging of the Spine and Spinal Cord , 2008, American Journal of Neuroradiology.

[11]  Jun Ma,et al.  Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model , 2010, Comput. Vis. Image Underst..

[12]  R. Eastell,et al.  Classification of vertebral fractures , 1991, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[13]  S. H. Kan,et al.  Epidemiology of vertebral fractures in women. , 1989, American journal of epidemiology.

[14]  Cristian Lorenz,et al.  Automated model-based vertebra detection, identification, and segmentation in CT images , 2009, Medical Image Anal..

[15]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[16]  Bostjan Likar,et al.  Analysis of Four Manual and a Computerized Method for Measuring Axial Vertebral Rotation in Computed Tomography Images , 2010, Spine.

[17]  T. Spector,et al.  The assessment of vertebral deformity: A method for use in population studies and clinical trials , 1993, Osteoporosis International.

[18]  Boštjan Likar,et al.  Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images , 2011, Physics in medicine and biology.

[19]  G F Jensen,et al.  Validity in diagnosing osteoporosis. Observer variation in interpreting spinal radiographs. , 1984, European journal of radiology.

[20]  G. Duckeck,et al.  Spine deformity index (SDI) versus other objective procedures of vertebral fracture identification in patients with osteoporosis: A comparative study , 1991, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.