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1.H. Labelle, C. Aubin, R. Jackson, L. Lenke, P. Newton, and S. Parent, “Seeing the spine in 3D: How will it change what we do?,” J. Pediatr. Orthop. 31, S37S45 (2011).
2.V. Pomero, D. Mitton, S. Laporte, J. A. de Guise, and W. Skalli, “Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric and statistic model,” Clin. Biomech. 19, 240247 (2004).
3.R. Dumas, B. Blanchard, R. Carlier, C. G. de Loubresse, J. C. L. Huec, C. Marty, M. Moinard, and J. M. Vital, “A semi-automated method using interpolation and optimisation for the 3D reconstruction of the spine from bi-planar radiography: A precision and accuracy study,” Med. Biol. Eng. Comput. 46, 8592 (2008).
4.L. Humbert, J. de Guise, B. Aubert, B. Godbout, and W. Skalli, “3D reconstruction of the spine from biplanar x-rays using parametric models based on transversal and longitudinal inferences,” Med. Eng. Phys. 31(6), 681687 (2009).
5.D. Moura, J. Boisvert, J. Barbosa, H. Labelle, and J. Tavares, “Fast 3D reconstruction of the spine from biplanar radiographs using a deformable articulated model,” Med. Eng. Phys. 33, 924933 (2011).
6.J. Boisvert and D. Moura, “Interactive 3D reconstruction of the spine from radio-graphs using a statistical shape model and second-order cone programming,” in 33rd Annual International Conference of the IEEE EMBS (IEEE, Piscataway, NJ, 2011), pp. 57265729.
7.S. Kadoury, F. Chariet, and H. Labelle, “Personalized x-ray 3D reconstruction of the scoliotic spine from statistical and image models,” IEEE Trans. Med. Imaging 28, 14221435 (2009).
8.F. Lecron, J. Boisvert, S. Mahmoudi, H. Labelle, and M. Benjelloun, “Fast 3D spine reconstruction of postoperative patients using a multilevel statistical model,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2012. Lecture Notes in Computer Science, edited byN. Ayache, H. Delingette, P. Golland, and K. Mori (Springer, Heidelberg, 2012), pp. 446453.
9.L. Cayton, “Algorithms for manifold learning,” Technical Report No. CS2008-0923 (University of California, San Diego, CA, 2005).
10.M. Brand and K. Huang, “A unifying theorem for spectral embedding and clustering,” in Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (2003).
11.N. Pitelis, C. Russell, and L. Agapito, “Learning a manifold as an atlas,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2013), pp. 18.
12.F. Perbet, S. Johnson, M.-T. Pham, and B. Stenger, “Human body shape estimation using a multi-resolution manifold forest,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Piscataway, NJ, 2014), pp. 668675.
13.S. Kadoury, H. Labelle, and S. Parent, “3D spine reconstruction of postoperative patients from multi-level manifold ensembles,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2014 (Springer, Berlin, 2014), pp. 361368.
14.S. Kadoury, F. Chariet, and H. Labelle, “Self-calibration of biplanar radiographic images through geometric spine shape descriptors,” IEEE Trans. Biomed. Eng. 57(7), 16631675 (2010).
15.A. Laurentini, “The visual hull concept for silhouette-based image understanding,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 150162 (1994).
16.C. Silpa-Anan and R. Hartley, “Optimised KD-trees for fast image descriptor matching,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, Piscataway, NJ, 2008), pp. 18.
17.C. Orsenigo and C. Vercellis, “Kernel ridge regression for out-of-sample mapping in supervised manifold learning,” Expert Syst. Appl. 39(9), 77577762 (2012).
18.E. Ferrante and N. Paragios, “Non-rigid 2D-3D medical image registration using Markov random fields,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013 (Springer, Berlin, 2013), pp. 163170.
19.S. Kadoury, H. Labelle, and N. Paragios, “Automatic inference of articulated spine models in CT images using high-order Markov random fields,” Med. Image Anal. 15, 426437 (2011).
20.N. Komodakis, N. Paragios, and G. Tziritas, “MRF energy minimization and beyond via dual decomposition,” IEEE Trans. PAMI 33(3), 531552 (2011).
21.S. Roweis and L. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 23232326 (2000).
22.S. Delorme, Y. Petit, J. A. de Guise, H. Labelle, C. E. Aubin, and J. Dansereau, “Assessment of the 3-D reconstruction and high-resolution geometrical modeling of the human skeletal trunk from 2-D radiographic images,” IEEE Trans. Biomed. Eng. 50, 989998 (2003).
23.T. Vrtovec et al., “Analysis of four manual and a computerized method for measuring axial vertebral rotation in computed tomography images,” Spine 35(12), E535E541 (2010).
24.T. Klinder, J. Ostermann, M. Ehm, A. Franz, R. Kneser, and C. Lorenz, “Automated model-based vertebra detection, identification, and segmentation in CT images,” Med. Image Anal. 13, 471482 (2009).

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The postoperative evaluation of scoliosis patients undergoing corrective treatment is an important task to assess the strategy of the spinal surgery. Using accurate 3D geometric models of the patient’s spine is essential to measure longitudinal changes in the patient’s anatomy. On the other hand, reconstructing the spine in 3D from postoperative radiographs is a challenging problem due to the presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine.

This paper describes the reconstruction problem by searching for the optimal model within a manifold space of articulated spines learned from a training dataset of pathological cases who underwent surgery. The manifold structure is implemented based on a multilevel manifold ensemble to structure the data, incorporating connections between nodes within a single manifold, in addition to connections between different multilevel manifolds, representing subregions with similar characteristics.

The reconstruction pipeline was evaluated on x-ray datasets from both preoperative patients and patients with spinal surgery. By comparing the method to ground-truth models, a 3Dreconstruction accuracy of 2.24 ± 0.90 mm was obtained from 30 postoperative scoliotic patients, while handling patients with highly deformed spines.

This paper illustrates how this manifoldmodel can accurately identify similar spine models by navigating in the low-dimensional space, as well as computing nonlinear charts within local neighborhoods of the embedded space during the testing phase. This technique allows postoperative follow-ups of spinal surgery using personalized 3D spine models and assess surgical strategies for spinal deformities.


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