Quantitative vertebral morphometry using neighbor-conditional shape models

Authors:
Marleen de Bruijne;Michael T. Lund;László B. Tankó;Paola P. Pettersen;Mads Nielsen
Affiliations:
IT University of Copenhagen, Denmark;IT University of Copenhagen, Denmark;Center for Clinical and Basic Research, Ballerup, Denmark;Center for Clinical and Basic Research, Ballerup, Denmark;IT University of Copenhagen, Denmark
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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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 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.