Localisation of Vertebrae on DXA VFA Images using Constrained Local Models with Random Forest Regression Voting

Osteoporotic fractures are associated with significant morbidity, mortality and public health costs, and will increase with an ageing population. Many osteoporotic vertebral fractures (VF) present on images do not come to clinical attention or lead to fracture prevention treatment. Furthermore, DXA vertebral fracture assessments (VFA) are often reported subjectively by a radiologist or other clinician. VFA computer-aided systems offer potential advantages. Methods based on statistical shape models (e.g. active appearance models, AAMs) have been used to segment vertebrae in radiographs and DXA VFA. However, results achieved using AAMs exhibit significant numbers of large errors due to model fitting failure, particularly on more severely fractured vertebrae. We evaluate an alternative algorithm, the Random Forest Regression Voting Constrained Local Model (RFRV-CLMs), which has proved more robust and generalizable than AAMs in annotation of landmarks on various clinical images; we investigate whether this will reduce the number of fitting failures in vertebral segmentation.