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3D Biplanar Reconstruction of Scoliotic Vertebrae Using Statistical Models

Said Benameur, Max Mignotte, Stefan Parent, Hubert Labelle, Wafa Skalli, Jacques A. de Guise
CVPR 2001 pp. II:577-582
doi:10.1109/CVPR.2001.991014 /cvpr/2001/benameur2001cvpr-d/

Abstract

This paper presents a new 3D reconstruction method of the scoliotic vertebrae of a spine, using two conventional radiographic views (postero-anterior and lateral), and global prior knowledge on the geometrical structure of each vertebra. This geometrical knowledge is efficiently captured by a statistical deformable template integrating a set of admissible deformations, expressed by the first modes of variation in the Karhunen-Loeve expansion of the pathological deformations observed on a representative scoliotic vertebra population. The proposed reconstruction method consists in fitting the projections of this deformable template with the segmented contours of the corresponding vertebra on the two radiographic views. The 3D reconstruction problem is stated as the minimization of a cost function for each vertebra and solved with a gradient descent technique. The reconstruction of the spine is then made vertebra by vertebra. This 3D reconstruction method has been successfully tested on several biplanar radiographic images, yielding very promising results.

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Cite

Text

Benameur et al. "3D Biplanar Reconstruction of Scoliotic Vertebrae Using Statistical Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.991014

Markdown

[Benameur et al. "3D Biplanar Reconstruction of Scoliotic Vertebrae Using Statistical Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/benameur2001cvpr-d/) doi:10.1109/CVPR.2001.991014

BibTeX

@inproceedings{benameur2001cvpr-d,
  title     = {{3D Biplanar Reconstruction of Scoliotic Vertebrae Using Statistical Models}},
  author    = {Benameur, Said and Mignotte, Max and Parent, Stefan and Labelle, Hubert and Skalli, Wafa and de Guise, Jacques A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2001},
  pages     = {II:577-582},
  doi       = {10.1109/CVPR.2001.991014},
  url       = {https://mlanthology.org/cvpr/2001/benameur2001cvpr-d/}
}