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



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.


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.


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.


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.


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



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)


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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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