Quantitative Vertebral Morphometry Using Neighbor-Conditional Shape Models

  • Marleen de Bruijne
  • Michael T. Lund
  • László B. Tankó
  • Paola P. Pettersen
  • Mads Nielsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


A novel method for vertebral fracture quantification from X-ray images is presented. Using pairwise conditional shape models trained on a set of healthy spines, the most likely normal vertebra shapes are estimated conditional on all other vertebrae in the image. The differences between the true shape and the reconstructed normal shape is subsequently used as a measure of abnormality. In contrast with the current (semi-)quantitative grading strategies this method takes the full shape into account, it uses a patient-specific reference by combining population-based information on biological variation in vertebra shape and vertebra interrelations, and it provides a continuous measure of deformity.

The method is demonstrated on 212 lateral spine radiographs with in total 78 fractures. The distance between prediction and true shape is 1.0 mm for unfractured vertebrae and 3.7 mm for fractures, which makes it possible to diagnose and assess the severity of a fracture.


Vertebral Fracture Assessment Active Shape Model True Shape Root Mean Square Distance Point Distribution 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marleen de Bruijne
    • 1
  • Michael T. Lund
    • 1
  • László B. Tankó
    • 2
  • Paola P. Pettersen
    • 2
  • Mads Nielsen
    • 1
  1. 1.IT University of CopenhagenDenmark
  2. 2.Center for Clinical and Basic ResearchBallerupDenmark

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