Predicting Spine Geometry and Scoliosis from DXA Scans
Amir Jamaludin Timor Kadir Emma Clark Andrew Zisserman
Our objective in this paper is to estimate spine curvature in DXA scans. To
this end we first train a neural network to predict the middle spine curve in
the scan, and then use an integral-based method to determine the curvature
along the spine curve. We use the curvature to compare to the standard angle
scoliosis measure obtained using the DXA Scoliosis Method (DSM). The
performance improves over the prior work of Jamaludin et al. 2018. We show that
the maximum curvature can be used as a scoring function for ordering the
severity of spinal deformation.