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3D Spine Reconstruction of Postoperative Patients from Multi-level Manifold Ensembles

  • Samuel Kadoury
  • Hubert Labelle
  • Stefan Parent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

The quantitative assessment of surgical outcomes using personalized anatomical models is an essential task for the treatment of spinal deformities such as adolescent idiopathic scoliosis. However an accurate 3D reconstruction of the spine from postoperative X-ray images remains challenging due to presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine. In this paper, we formulate the reconstruction problem as an optimization over a manifold of articulated spine shapes learned from pathological training data. The manifold itself is represented using a novel data structure, a multi-level manifold ensemble, which contains links between nodes in a single hierarchical structure, as well as links between different hierarchies, representing overlapping partitions. We show that this data structure allows both efficient localization and navigation on the manifold, for on-the-fly building of local nonlinear models (manifold charting). Our reconstruction framework was tested on pre- and postoperative X-ray datasets from patients who underwent spinal surgery. Compared to manual ground-truth, our method achieves a 3D reconstruction accuracy of 2.37 ±0.85mm for postoperative spine models and can deal with severe cases of scoliosis.

Keywords

Adolescent Idiopathic Scoliosis Markov Random Field Ambient Space Locally Linear Embedding Visual Hull 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Samuel Kadoury
    • 1
    • 2
  • Hubert Labelle
    • 2
  • Stefan Parent
    • 2
  1. 1.MEDICALPolytechnique MontrealMontrealCanada
  2. 2.Sainte-Justine Hospital Research CenterMontrealCanada

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