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Quantitative vertebral morphometry using neighbor-conditional shape models.

Authors
  • de Bruijne, Marleen1
  • Lund, Michael T
  • Tankó, László B
  • Pettersen, Paola C
  • Nielsen, Mads
  • 1 Department of Computer Science, University of Copenhagen, Denmark. [email protected] , (Denmark)
Type
Published Article
Journal
Medical Image Analysis
Publisher
Elsevier
Publication Date
Oct 01, 2007
Volume
11
Issue
5
Pages
503–512
Identifiers
PMID: 17720611
Source
Medline
License
Unknown

Abstract

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 the shapes of all other vertebrae in the image. The difference 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 develops a patient-specific reference by combining population-based information on biological variation in vertebral shape and vertebra interrelations, and it provides a continuous measure of deformity. The method is demonstrated on 282 lateral spine radiographs with in total 93 fractures. Vertebral fracture detection is shown to be in good agreement with semi-quantitative scoring by experienced radiologists and is superior to the performance of shape models alone.

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