A hierarchical statistical modeling approach for the unsupervised 3D reconstruction of the scoliotic spine

In this paper, we propose a new and accurate 3D reconstruction technique for the scoliotic spine from a pair planar and conventional radiographic images (postero-anterior and lateral). The proposed model uses a priori hierarchical global knowledge, both on the geometric structure of the whole spine and of each vertebra. More precisely, it relies on the specification of two 3D templates. The first, a rough geometric template on which rigid admissible deformations are defined, is used to ensure a crude registration of the whole spine. 3D reconstruction is then refined for each vertebra, by a template on which nonlinear admissible global deformations are modeled, with statistical modal analysis of the pathological deformations observed on a representative scoliotic vertebra population. This unsupervised coarse-to-fine 3D reconstruction procedure is stated as a double energy function minimization problems efficiently solved with a stochastic optimization algorithm. The proposed method, tested on several pairs of biplanar radiographic images with scoliotic deformities, is comparable in terms of accuracy with the classical CT-scan technique while being unsupervised and requiring a lower amount of radiation for the patient.

[1]  Stéphane Lavallée,et al.  Nonrigid 3-D/2-D Registration of Images Using Statistical Models , 1999, MICCAI.

[2]  Moshe Sipper,et al.  Evolutionary computation in medicine: an overview , 2000, Artif. Intell. Medicine.

[3]  D. M. Keenan,et al.  Towards automated image understanding , 1989 .

[4]  Jean-Louis Coatrieux,et al.  Three-dimensional reconstruction of the left ventricle from two angiographic views: an evidence combination approach , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Yvan Petit,et al.  Assessment of the 3-D reconstruction and high-resolution geometrical modeling of the human skeletal trunk from 2-D radiographic images , 2003, IEEE Transactions on Biomedical Engineering.

[6]  Anil K. Jain,et al.  Object Matching Using Deformable Templates , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  François Destrempes.  Unsupervised Localization of Shapes Using Statistical Models , 2002 .

[9]  Max Mignotte,et al.  3D biplanar reconstruction of scoliotic vertebrae using statistical models , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Ralph Benjamin.  Object-Based 3D X-Ray Imaging , 1995, CVRMed.

[11]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[12]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[13]  M M Panjabi,et al.  Thoracic Human Vertebrae Quantitative Three‐Dimensional Anatomy , 1991, Spine.

[14]  Jake K. Aggarwal,et al.  TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2008 .

[15]  Heinz U. Lemke,et al.  3D reconstruction of the femoral bone using two X-ray images from orthogonal views , 1996 .

[16]  Demetri Terzopoulos,et al.  Constraints on Deformable Models: Recovering 3D Shape and Nonrigid Motion , 1988, Artif. Intell..

[17]  P. Cinquin,et al.  Matching Of Medical Images For Computed And Robot Assisted Surgery , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[18]  J. Dansereau,et al.  Incorporation of patient displacement into a trunk reconstruction technique , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[19]  David Cox,et al.  Applied Statistics - Principles and Examples , 1981 .

[20]  Richard M. Leahy,et al.  Surface-based labeling of cortical anatomy using a deformable atlas , 1997, IEEE Transactions on Medical Imaging.

[21]  Max Mignotte,et al.  3D/2D registration and segmentation of scoliotic vertebrae using statistical models. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[22]  Genaro Tolentino Marzan,et al.  Rational Design for Close-Range Photogrammetry , 1976 .

[23]  Jian Liu,et al.  Image denoising based on scale-space mixture modeling of wavelet coefficients , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[24]  Dinggang Shen,et al.  Hierarchical active shape models, using the wavelet transform , 2003, IEEE Transactions on Medical Imaging.

[25]  Anna Maria Fanelli,et al.  Computer-aided simulation for bone surgery , 1993, IEEE Computer Graphics and Applications.

[26]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[27]  Yvan Petit,et al.  Three-dimensional (3-D) reconstruction of the spine from a single X-ray image and prior vertebra models , 2004, IEEE Transactions on Biomedical Engineering.

[28]  O. François.  GLOBAL OPTIMIZATION WITH EXPLORATION/SELECTION ALGORITHMS AND SIMULATED ANNEALING , 2002 .

[29]  Jyrki Lötjönen,et al.  Model extraction from magnetic resonance volume data using the deformable pyramid , 1999, Medical Image Anal..

[30]  Laurent D. Cohen,et al.  A Parametric Deformable Model to Fit Unstructured 3D Data , 1998, Comput. Vis. Image Underst..

[31]  Max Mignotte,et al.  Endocardial Boundary E timation and Tracking in Echocardiographic Images using Deformable Template and Markov Random Fields , 2001, Pattern Analysis & Applications.

[32]  V. Cassar-Pullicino,et al.  Imaging in scoliosis: what, why and how? , 2002, Clinical radiology.

[33]  Hubert Labelle,et al.  Morphometric Analysis of Anatomic Scoliotic Specimens , 2002, Spine.

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

[35]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[36]  J.A. de Guise,et al.  3D-biomedical modeling: merging image processing and computer aided design , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[37]  I A Stokes,et al.  Measurements of the three-dimensional shape of the rib cage. , 1988, Journal of biomechanics.

[38]  Bostjan Likar,et al.  Segmenting Articulated Structures by Hierarchical Statistical Modeling of Shape, Appearance, and Topology , 2001, MICCAI.

[39]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Y. Kita,et al.  Elastic-Model Driven Analysis of Several Views of a Deformable Cylindrical Object , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  M M Panjabi,et al.  Human Lumbar Vertebrae: Quantitative Three-Dimensional Anatomy , 1992, Spine.

[42]  Charles Kervrann,et al.  A hierarchical statistical framework for the segmentation of deformable objects in image sequences , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Patrick Pérez,et al.  Hierarchical statistical models for the fusion of multiresolution image data , 1995, Proceedings of IEEE International Conference on Computer Vision.