Computational Methods and Clinical Applications for Spine Imaging

Spinal fusion combined with vertebral fixation through pedicle screw placement is the preferred surgical treatment for several spinal deformities. The accuracy of pedicle screw placement is directly related to the surgical outcome, however, manual planning of screw size and trajectory is time-consuming, while automated approaches do not take into account the screw fastening strength. We propose a novel automated method for optimal planning of pedicle screw size and trajectory that takes into account both geometric (i.e. morphometry) and anatomical (i.e. bone mineral density) properties of vertebrae to maximize the screw fastening strength. The size and trajectory of 61 pedicle screws, determined by the automated method in computed tomography images of nine patients, were in high agreement with preoperative manual plans defined by a spine surgeon (mean difference of 0.6 mm in diameter, 4.0 mm in length, 1.7 mm in pedicle crossing, and (6.1◦)in screw insertion angles), and an increased fastening strength was observed for 50 cases (82 %).

[1]  Nicholas Ayache,et al.  Generation of a statistical shape model with probabilistic point correspondences and the expectation maximization- iterative closest point algorithm , 2007, International Journal of Computer Assisted Radiology and Surgery.

[2]  Hervé Delingette,et al.  General Object Reconstruction Based on Simplex Meshes , 1999, International Journal of Computer Vision.

[3]  W. Kalender,et al.  Accuracy limits for the determination of cortical width and density: the influence of object size and CT imaging parameters. , 1999, Physics in medicine and biology.

[4]  Steven K Boyd,et al.  Automatic segmentation of cortical and trabecular compartments based on a dual threshold technique for in vivo micro-CT bone analysis. , 2007, Bone.

[5]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[6]  Purang Abolmaesumi,et al.  Group-Wise Registration of Point Sets for Statistical Shape Models , 2012, IEEE Transactions on Medical Imaging.

[7]  Tim Cootes,et al.  Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation , 2010, Osteoporosis International.

[8]  J. Gower Generalized procrustes analysis , 1975 .

[9]  Cedric Schwartz,et al.  An Automated Statistical Shape Model Developmental Pipeline: Application to the Human Scapula and Humerus , 2015, IEEE Transactions on Biomedical Engineering.

[10]  Andrew H. Gee,et al.  High resolution cortical bone thickness measurement from clinical CT data , 2010, Medical Image Anal..

[11]  Nadia Magnenat-Thalmann,et al.  Medical image analysis , 1999, Medical Image Anal..

[12]  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..

[13]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[14]  Joseph E. Burns,et al.  Automated detection of sclerotic metastases in the thoracolumbar spine at CT. , 2013, Radiology.

[15]  T N Hangartner,et al.  Thresholding technique for accurate analysis of density and geometry in QCT, pQCT and microCT images. , 2007, Journal of musculoskeletal & neuronal interactions.

[16]  Boštjan Likar,et al.  AUTOMATED CONSTRUCTION OF 3D STATISTICAL SHAPE MODELS , 2011 .

[17]  Ronald M. Summers,et al.  Automated spinal column extraction and partitioning , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[18]  Jürgen Weese,et al.  Automated 3-D PDM construction from segmented images using deformable models , 2003, IEEE Transactions on Medical Imaging.

[19]  Cristian Lorenz,et al.  Generation of Point-Based 3D Statistical Shape Models for Anatomical Objects , 2000, Comput. Vis. Image Underst..

[20]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[21]  Dawn M Elliott,et al.  Human L3L4 intervertebral disc mean 3D shape, modes of variation, and their relationship to degeneration. , 2014, Journal of biomechanics.

[22]  Simon Fuhrmann,et al.  Automatic Construction of Statistical Shape Models for Vertebrae , 2011, MICCAI.

[23]  Timothy F. Cootes,et al.  A minimum description length approach to statistical shape modeling , 2002, IEEE Transactions on Medical Imaging.

[24]  Olivier Guyen,et al.  Contribution of Trabecular and Cortical Components to Biomechanical Behavior of Human Vertebrae: An Ex Vivo Study , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[25]  Timothy F. Cootes,et al.  Training Models of Shape from Sets of Examples , 1992, BMVC.

[26]  S. Vadlamani On the Diffusion of Shape , 2007 .

[27]  Charles Baur,et al.  A statistical shape model of the human second cervical vertebra , 2015, International Journal of Computer Assisted Radiology and Surgery.

[28]  Andrew H. Gee,et al.  Imaging the femoral cortex: Thickness, density and mass from clinical CT , 2012, Medical Image Anal..

[29]  Hui Huang,et al.  Laplacian Operator Based Level Set Segmentation Algorithm for Medical Images , 2009, 2009 2nd International Congress on Image and Signal Processing.

[30]  Martin Styner,et al.  Particle-Based Shape Analysis of Multi-object Complexes , 2008, MICCAI.

[31]  Andrew Zisserman,et al.  Vertebrae Detection and Labelling in Lumbar MR Images , 2014 .

[32]  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..