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European Spine Journal

, Volume 28, Issue 4, pp 658–664 | Cite as

Quasi-automatic 3D reconstruction of the full spine from low-dose biplanar X-rays based on statistical inferences and image analysis

  • Laurent GajnyEmail author
  • Shahin Ebrahimi
  • Claudio Vergari
  • Elsa Angelini
  • Wafa Skalli
Original Article

Abstract

Purpose

To design a quasi-automated three-dimensional reconstruction method of the spine from biplanar X-rays as the daily used method in clinical routine is based on manual adjustments of a trained operator and the reconstruction time is more than 10 min per patient.

Methods

The proposed method of 3D reconstruction of the spine (C3–L5) relies first on a new manual input strategy designed to fit clinicians’ skills. Then, a parametric model of the spine is computed using statistical inferences, image analysis techniques and fast manual rigid registration.

Results

An agreement study with the clinically used method on a cohort of 57 adolescent scoliotic subjects has shown that both methods have similar performance on vertebral body position and axial rotation (null bias in both cases and standard deviation of signed differences of 1 mm and 3.5° around, respectively). In average, the solution could be computed in less than 5 min of operator time, even for severe scoliosis.

Conclusion

The proposed method allows fast and accurate 3D reconstruction of the spine for wide clinical applications and represents a significant step towards full automatization of 3D reconstruction of the spine. Moreover, it is to the best of our knowledge the first method including also the cervical spine.

Graphical abstract

These slides can be retrieved under electronic supplementary material.

Keywords

Scoliosis 3D reconstruction Statistical inferences Landmark detection Biplanar X-rays 

Notes

Acknowledgements

The authors thank the ParisTech BiomecAM chair program, on subject-specific musculoskeletal modelling and in particular Société Générale and COVEA. The authors would also like to thank Aurélien Laville for having initiated this work.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest to declare.

Supplementary material

586_2018_5807_MOESM1_ESM.pptx (989 kb)
Supplementary material 1 (PPTX 988 kb)

References

  1. 1.
    Stokes IA (1994) Three-dimensional terminology of spinal deformity. A report presented to the Scoliosis Research Society by the Scoliosis Research Society Working Group on 3-D terminology of spinal deformity. Spine 19:236–248CrossRefGoogle Scholar
  2. 2.
    Ilharreborde B, Sebag G, Skalli W, Mazda K (2013) Adolescent idiopathic scoliosis treated with posteromedial translation: radiologic evaluation with a 3D low-dose system. Eur Spine J 22:2382–2391.  https://doi.org/10.1007/s00586-013-2776-7 CrossRefGoogle Scholar
  3. 3.
    Illés T, Tunyogi-Csapó M, Somoskeöy S (2011) Breakthrough in three-dimensional scoliosis diagnosis: significance of horizontal plane view and vertebra vectors. Eur Spine J 20:135–143.  https://doi.org/10.1007/s00586-010-1566-8 CrossRefGoogle Scholar
  4. 4.
    Skalli W, Vergari C, Ebermeyer E et al (2017) Early detection of progressive adolescent idiopathic scoliosis: a severity index. Spine 42:823–830.  https://doi.org/10.1097/BRS.0000000000001961 CrossRefGoogle Scholar
  5. 5.
    Lafon Y, Steib J-P, Skalli W (2010) Intraoperative three dimensional correction during in situ contouring surgery by using a numerical model. Spine 35:453–459.  https://doi.org/10.1097/BRS.0b013e3181b8eaca CrossRefGoogle Scholar
  6. 6.
    Amabile C, Huec J-CL, Skalli W (2016) Invariance of head-pelvis alignment and compensatory mechanisms for asymptomatic adults older than 49 years. Eur Spine J.  https://doi.org/10.1007/s00586-016-4830-8 Google Scholar
  7. 7.
    Schwab F, Farcy J-P, Bridwell K et al (2006) A clinical impact classification of scoliosis in the adult. Spine 31:2109–2114.  https://doi.org/10.1097/01.brs.0000231725.38943.ab CrossRefGoogle Scholar
  8. 8.
    Hanaoka S, Masutani Y, Nemoto M et al (2017) Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images. Int J Comput Assist Radiol Surg 12:413–430.  https://doi.org/10.1007/s11548-016-1507-z CrossRefGoogle Scholar
  9. 9.
    Brenner DJ, Hall EJ (2007) Computed tomography—an increasing source of radiation exposure. N Engl J Med 357:2277–2284.  https://doi.org/10.1056/NEJMra072149 CrossRefGoogle Scholar
  10. 10.
    Yazici M, Acaroglu ER, Alanay A et al (2001) Measurement of vertebral rotation in standing versus supine position in adolescent idiopathic scoliosis. J Pediatr Orthop 21:252–256Google Scholar
  11. 11.
    Dubousset J, Charpak G, Dorion I et al (2005) A new 2D and 3D imaging approach to musculoskeletal physiology and pathology with low-dose radiation and the standing position: the EOS system. Bull Acad Natl Med 189:287–297 (discussion 297–300) Google Scholar
  12. 12.
    Humbert L, De Guise JA, Aubert B et al (2009) 3D reconstruction of the spine from biplanar X-rays using parametric models based on transversal and longitudinal inferences. Med Eng Phys 31:681–687.  https://doi.org/10.1016/j.medengphy.2009.01.003 CrossRefGoogle Scholar
  13. 13.
    Ilharreborde B, Steffen JS, Nectoux E et al (2011) Angle measurement reproducibility using EOS three-dimensional reconstructions in adolescent idiopathic scoliosis treated by posterior instrumentation. Spine 36:E1306–E1313.  https://doi.org/10.1097/BRS.0b013e3182293548 CrossRefGoogle Scholar
  14. 14.
    Carreau JH, Bastrom T, Petcharaporn M et al (2014) Computer-generated, three-dimensional spine model from biplanar radiographs: a validity study in idiopathic scoliosis curves greater than 50 degrees. Spine Deform 2:81–88.  https://doi.org/10.1016/j.jspd.2013.10.003 CrossRefGoogle Scholar
  15. 15.
    Ferrero E, Lafage R, Vira S et al (2016) Three-dimensional reconstruction using stereoradiography for evaluating adult spinal deformity: a reproducibility study. Eur Spine J.  https://doi.org/10.1007/s00586-016-4833-5 Google Scholar
  16. 16.
    Kadoury S, Cheriet F, Labelle H (2009) Personalized X-ray 3-D reconstruction of the scoliotic spine from hybrid statistical and image-based models. IEEE Trans Med Imaging 28:1422–1435.  https://doi.org/10.1109/TMI.2009.2016756 CrossRefGoogle Scholar
  17. 17.
    Moura DC, Barbosa JG (2014) Real-scale 3D models of the scoliotic spine from biplanar radiography without calibration objects. Comput Med Imaging Graph 38:580–585.  https://doi.org/10.1016/j.compmedimag.2014.05.007 CrossRefGoogle Scholar
  18. 18.
    Lecron F, Boisvert J, Mahmoudi S et al (2013) Three-dimensional spine model reconstruction using one-class SVM regularization. IEEE Trans Biomed Eng 60:3256–3264.  https://doi.org/10.1109/TBME.2013.2272657 CrossRefGoogle Scholar
  19. 19.
    Aubert B, Vidal PA, Parent S et al (2017) Convolutional neural network and in-painting techniques for the automatic assessment of scoliotic spine surgery from biplanar radiographs. In: Medical image computing and computer-assisted intervention—MICCAI 2017. Springer, Cham, pp 691–699Google Scholar
  20. 20.
    Barrey C, Jund J, Noseda O, Roussouly P (2007) Sagittal balance of the pelvis-spine complex and lumbar degenerative diseases. A comparative study about 85 cases. Eur Spine J 16:1459–1467.  https://doi.org/10.1007/s00586-006-0294-6 CrossRefGoogle Scholar
  21. 21.
    Lafage V, Schwab F, Skalli W et al (2008) Standing balance and sagittal plane spinal deformity: analysis of spinopelvic and gravity line parameters. Spine 33:1572–1578.  https://doi.org/10.1097/BRS.0b013e31817886a2 CrossRefGoogle Scholar
  22. 22.
    Canavese F, Turcot K, De Rosa V et al (2011) Cervical spine sagittal alignment variations following posterior spinal fusion and instrumentation for adolescent idiopathic scoliosis. Eur Spine J 20:1141–1148.  https://doi.org/10.1007/s00586-011-1837-z CrossRefGoogle Scholar
  23. 23.
    Rousseau M, Laporte S, Chavary-bernier E et al (2007) Reproducibility of measuring the shape and three-dimensional position of cervical vertebrae in upright position using the EOS stereoradiography system. Spine 32:2569–2572.  https://doi.org/10.1097/BRS.0b013e318158cba2 CrossRefGoogle Scholar
  24. 24.
    Lüthi M, Gerig T, Jud C, Vetter T (2018) Gaussian process morphable models. IEEE Trans Pattern Anal Mach Intell.  https://doi.org/10.1109/tpami.2017.2739743 Google Scholar
  25. 25.
    Ebrahimi S, Angelini E, Gajny L, Skalli W (2016) Lumbar spine posterior corner detection in X-rays using Haar-based features. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI), pp 180–183Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut de Biomécanique Humaine Georges CharpakArts et Métiers ParisTechParisFrance
  2. 2.LTCI, Telecom ParisTechUniversité Paris-SaclayParisFrance
  3. 3.ITMAT Data Science Group, NIHR Imperial BRCImperial College LondonLondonUK

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