K. S. Arun, T. S. Huang, and S. D. Blostein, Least-Squares Fitting of Two 3-D Point Sets, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PAMI-9, issue.5, pp.698-700, 1987.

V. Blanz and T. Vetter, A morphable model for the synthesis of 3D faces, Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH '99, pp.187-194, 1999.

F. Bogo, M. J. Black, M. Loper, and J. Romero, Detailed Full-Body Reconstructions of Moving People from Monocular RGB-D Sequences, 2015 IEEE International Conference on Computer Vision (ICCV), pp.2300-2308, 2015.

M. N. Bossa and S. Olmos, MULTI-OBJECT STATISTICAL POSE+SHAPE MODELS, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1204-1207, 2007.

P. Bruners, T. Penzkofer, M. Nagel, R. Elfring, N. Gronloh et al., Electromagnetic tracking for CT-guided spine interventions: phantom, ex-vivo and in-vivo results, European Radiology, vol.19, issue.4, pp.990-994, 2008.

J. Q. Campbell and A. J. Petrella, Automated finite element modeling of the lumbar spine: Using a statistical shape model to generate a virtual population of models, Journal of Biomechanics, vol.49, issue.13, pp.2593-2599, 2016.

Y. Cai, S. Osman, M. Sharma, M. Landis, and S. Li, Multi-Modality Vertebra Recognition in Arbitrary Views Using 3D Deformable Hierarchical Model, IEEE Transactions on Medical Imaging, vol.34, issue.8, pp.1676-1693, 2015.

I. Castro-mateos, J. M. Pozo, M. Pereanez, K. Lekadir, A. F. Lazary et al., Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation, IEEE Transactions on Medical Imaging, vol.34, issue.8, pp.1663-1675, 2015.

B. Glocker, J. Feulner, A. Criminisi, D. R. Haynor, and E. Konukoglu, Automatic Localization and Identification of Vertebrae in Arbitrary Field-of-View CT Scans, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2012, pp.590-598, 2012.

T. Heimann and H. Meinzer, Statistical shape models for 3D medical image segmentation: A review, Medical Image Analysis, vol.13, issue.4, pp.543-563, 2009.

J. F. Hollenbeck, C. M. Cain, J. A. Fattor, P. J. Rullkoetter, and P. J. Laz, Statistical shape modeling characterizes three-dimensional shape and alignment variability in the lumbar spine, Journal of Biomechanics, vol.69, pp.146-155, 2018.

S. Kadoury, H. Labelle, and N. Paragios, Automatic inference of articulated spine models in CT images using high-order Markov Random Fields, Medical Image Analysis, vol.15, issue.4, pp.426-437, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00856308

T. Klinder, J. Ostermann, M. Ehm, A. Franz, R. Kneser et al., Automated model-based vertebra detection, identification, and segmentation in CT images, Medical Image Analysis, vol.13, issue.3, pp.471-482, 2009.

R. Korez, B. Ibragimov, B. Likar, F. Pernus, and T. Vrtovec, A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation, IEEE Transactions on Medical Imaging, vol.34, issue.8, pp.1649-1662, 2015.

M. Kirschner, M. Becker, and S. Wesarg, 3D Active Shape Model Segmentation with Nonlinear Shape Priors, Lecture Notes in Computer Science, pp.492-499, 2011.

M. Lüthi, T. Albrecht, and T. Vetter, Building Shape Models from Lousy Data, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2009, pp.1-8, 2009.

M. Hengameh, M. Wels, T. Heimann, B. M. Kelm, and M. Suehling, Fast and robust 3D vertebra segmentation using statistical shape models, 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp.3379-3382, 2013.

P. Probabilistic,

A. Rasoulian, R. Rohling, and P. Abolmaesumi, Lumbar Spine Segmentation Using a Statistical Multi-Vertebrae Anatomical Shape+Pose Model, IEEE Transactions on Medical Imaging, vol.32, issue.10, pp.1890-1900, 2013.

S. Ruiz-españa, J. Domingo, A. Díaz-parra, E. Dura, V. D'ocón-alcañiz et al., Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression, Medical Physics, vol.44, issue.9, pp.4695-4707, 2017.

. Sekuboyina, arXiv moves, Materials Today, vol.4, issue.5, p.20, 2001.

S. Seifert, A. Barbu, S. K. Zhou, D. Liu, J. Feulner et al., Hierarchical parsing and semantic navigation of full body CT data, Medical Imaging 2009: Image Processing, 2009.

S. Schmidt, J. Kappes, M. Bergtholdt, V. Pekar, S. Dries et al., Spine Detection and Labeling Using a Parts-Based Graphical Model, Lecture Notes in Computer Science, pp.122-133, 2007.

W. Stacklies, H. Redestig, M. Scholz, D. Walther, and J. Selbig, pcaMethods a bioconductor package providing PCA methods for incomplete data, Bioinformatics, vol.23, issue.9, pp.1164-1167, 2007.

M. E. Tipping and C. M. Bishop, Probabilistic Principal Component Analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.61, issue.3, pp.611-622, 1999.

Y. Zhan, D. Maneesh, M. Harder, and X. S. Zhou, Robust MR Spine Detection Using Hierarchical Learning and Local Articulated Model, Medical Image Computing and Computer-Assisted Intervention ? MICCAI 2012, pp.141-148, 2012.