Osteoporotic vertebral fractures are associated with an increased mortality, morbidity, a decrease in quality of life and are strong predictors of future osteoporotic fractures. Many guidelines on management of osteoporosis recommend pharmacological intervention when vertebral fractures are present. However, they remain largely underdiagnosed, due to frequently occurring asymptomatic and often not being reported by clinicians when present in computed tomography (CT) scans. The aim of this study is to explore the use of computer-aided diagnosis (CAD) systems to assist clinicians with detection and assessment of vertebral fractures based on CT images, thereby enabling preventive treatment and improving the outcome for osteoporosis patients. In the first part of this work we developed an automated method that performs Genant's semiquantitative method, one of the most widely used methods for assessment of vertebral fractures today. Results on a test set of 12 patients (116 vertebrae) show that the method is able to grade vertebral fractures with substantial agreement with two radiologists, with a quadratic weighted kappa value of 0.92. In the second part of this work we address the most notable limitations of Genant's semiquantitative method. We developed an automated scoring method that aims to bring both convenience and consistency to the field. The scoring method compares the shape of the vertebral body with the expected shape to calculate an abnormality score, which quantifies collapse of the vertebra and can be used to identify fractures. We again evaluated the scoring method on a test set of 12 patients (116 vertebrae) and show that the abnormality score corresponds well with assessment of two radiologists, with a quadratic weighted kappa value of 0.85 while addressing the main limitations of Genant's semiquantitative method.
Automated Detection and Assessment of Vertebral Fractures in CT Images