Vertebrae Segmentation in 3D CT Images Based on a Variational Framework

  • Kerstin HammernikEmail author
  • Thomas Ebner
  • Darko Stern
  • Martin Urschler
  • Thomas Pock
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 20)


Automatic segmentation of 3D vertebrae is a challenging task in medical imaging. In this paper, we introduce a total variation (TV) based framework that incorporates an a priori model, i.e., a vertebral mean shape, image intensity and edge information. The algorithm was evaluated using leave-one-out cross validation on a data set containing ten computed tomography scans and ground truth segmentations provided for the CSI MICCAI 2014 spine and vertebrae segmentation challenge. We achieve promising results in terms of the Dice Similarity Coefficient (DSC) of \(0.93 \pm 0.04\) averaged over the whole data set.


Shape Model Trabecular Bone Intensity Dice Similarity Coefficient Variational Framework Statistical Shape Model 
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.



This work was supported by the Austrian Science Fund (FWF) under the START project BIVISION, No. Y729, by the city of Graz (A16-21628/2013), and a European Community FP7 Marie Curie Intra European Fellowship (331239).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kerstin Hammernik
    • 1
    Email author
  • Thomas Ebner
    • 1
  • Darko Stern
    • 1
  • Martin Urschler
    • 2
  • Thomas Pock
    • 1
  1. 1.Institute for Computer Graphics and Vision, BioTechMedGraz University of TechnologyGrazAustria
  2. 2.Ludwig Boltzmann Institute for Clinical Forensic ImagingGrazAustria

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