KEYWORDS: Bone, Data modeling, Diagnostics, Received signal strength, Error analysis, Reconstruction algorithms, Statistical analysis, Radiography, Shape analysis, Basic research
Accurate and reliable identification and quantification of vertebral
fractures constitute a challenge both in clinical trials and in
diagnosis of osteoporosis. Various efforts have been made to develop
reliable, objective, and reproducible methods for assessing
vertebral fractures, but at present there is no consensus concerning
a universally accepted diagnostic definition of vertebral fractures.
In this project we want to investigate whether or not it is possible
to accurately reconstruct the shape of a normal vertebra, using a
neighbouring vertebra as prior information. The reconstructed shape
can then be used to develop a novel vertebra fracture measure, by
comparing the segmented vertebra shape with its reconstructed normal
shape. The vertebrae in lateral x-rays of the lumbar spine were
manually annotated by a medical expert. With this dataset we
built a shape model, with equidistant point distribution between the four corner points. Based on the shape model, a multiple linear
regression model of a normal vertebra shape was developed for each
dataset using leave-one-out cross-validation. The reconstructed shape
was calculated for each dataset using these regression models. The
average prediction error for the annotated shape was on average 3%.
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