Detailed Vertebral Segmentation Using Part-Based Decomposition and Conditional Shape Models

  • Marco PereañezEmail author
  • Karim Lekadir
  • Corné Hoogendoorn
  • Isaac Castro-Mateos
  • Alejandro Frangi
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)


With the advances in minimal invasive surgical procedures, accurate and detailed extraction of the vertebral boundaries is required. In practice, this is a difficult challenge due to the highly complex geometry of the vertebrae, in particular at the processes. This paper presents a statistical modeling approach for detailed vertebral segmentation based on part decomposition and conditional models. To this end, a Vononoi decomposition approach is employed to ensure that each of the main subparts the vertebrae is identified in the subdivision. The obtained shape constraints are effectively relaxed, allowing for an improved encoding of the fine details and shape variability at all the regions of the vertebrae. Subsequently, in order to maintain the statistical coherence of the ensemble, conditional models are used to model the statistical inter-relationships between the different subparts. For shape reconstruction and segmentation, a robust model fitting procedure is introduced to exclude outlying inter-part relationships in the estimation of the shape parameters. The experimental results based on a database of 30 CT scans show significant improvement in accuracy with respect to the state-of-the-art and the potential of the proposed technique for detailed vertebral modeling.


Shape Parameter Minimal Invasive Surgical Procedure Fine Detail Conditional Model Shape Reconstruction 
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

  • Marco Pereañez
    • 1
    Email author
  • Karim Lekadir
    • 2
  • Corné Hoogendoorn
    • 2
  • Isaac Castro-Mateos
    • 3
  • Alejandro Frangi
    • 3
  1. 1.Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)Universitat Pompeu FabraBarcelonaSpain
  2. 2.CISTIBUniversitat Pompeu FabraBarcelonaSpain
  3. 3.CISTIBUniversity of SheffieldSheffieldUK

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