Quantitative vertebral morphometry using neighbor-conditional shape models.
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.
de Bruijne
Marleen
M
Department of Computer Science, University of Copenhagen, Denmark. marleen@diku.dk
Lund
Michael T
MT
Tankó
László B
LB
Pettersen
Paola C
PC
Nielsen
Mads
M
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging*; D008954:Models, Biological; D015233:Models, Statistical; D010024:Osteoporosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016103:Spinal Fractures / Q000000981:diagnostic imaging*; D013382:Subtraction Technique