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Stacked Auto-encoders for Classification of 3D Spine Models in Adolescent Idiopathic Scoliosis

  • William E. Thong
  • Hubert Labelle
  • Jesse Shen
  • Stefan Parent
  • Samuel KadouryEmail author
Chapter
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Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)

Abstract

Current classification systems for adolescent idiopathic scoliosis lack information on how the spine is deformed in three dimensions (3D), which can mislead further treatment recommendations. We propose an approach to address this issue by a deep learning method for the classification of 3D spine reconstructions of patients. A low-dimensional manifold representation of the spine models was learnt by stacked auto-encoders. A K-Means++ algorithm using a probabilistic seeding method clustered the low-dimensional codes to discover sub-groups in the studied population. We evaluated the method with a case series analysis of 155 patients with Lenke Type-1 thoracic spinal deformations recruited at our institution. The clustering algorithm proposed 5 sub-groups from the thoracic population, yielding statistically significant differences in clinical geometric indices between all clusters. These results demonstrate the presence of 3D variability within a pre-defined 2D group of spinal deformities.

Keywords

Adolescent Idiopathic Scoliosis Cobb Angle Cluster Centroid Deep Neural Network Local Linear Embedding 
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 2015

Authors and Affiliations

  • William E. Thong
    • 1
  • Hubert Labelle
    • 2
  • Jesse Shen
    • 2
  • Stefan Parent
    • 2
  • Samuel Kadoury
    • 3
    Email author
  1. 1.MEDICALÉcole Polytechnique de MontréalMontréalCanada
  2. 2.Department of Surgery, CHU Sainte-Justine Research CenterUniversité de MontréalMontréalCanada
  3. 3.CHU Sainte-Justine Research Center, MEDICALÉcole Polytechnique de MontréalMontréalCanada

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