Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT.

OBJECTIVES: To develop a prototype algorithm for automatic spine segmentation in MDCT images and use it to automatically detect osteoporotic vertebral fractures.
METHODS: Cross-sectional routine thoracic and abdominal MDCT images of 71 patients including 8 males and 9 females with 25 osteoporotic vertebral fractures and longitudinal MDCT images of 9 patients with 18 incidental fractures in the follow-up MDCT were retrospectively selected. The spine segmentation algorithm localised and identified the vertebrae T5-L5. Each vertebra was automatically segmented by using corresponding vertebra surface shape models that were adapted to the original images. Anterior, middle, and posterior height of each vertebra was automatically determined; the anterior-posterior ratio (APR) and middle-posterior ratio (MPR) were computed. As the gold standard, radiologists graded vertebral fractures from T5 to L5 according to the Genant classification in consensus.
RESULTS: Using ROC analysis to differentiate vertebrae without versus with prevalent fracture, AUC values of 0.84 and 0.83 were obtained for APR and MPR, respectively (p < 0.001). Longitudinal changes in APR and MPR were significantly different between vertebrae without versus with incidental fracture (ΔAPR: -8.5 % ± 8.6 % versus -1.6 % ± 4.2 %, p = 0.002; ΔMPR: -11.4 % ± 7.7 % versus -1.2 % ± 1.6 %, p < 0.001).
CONCLUSIONS: This prototype algorithm may support radiologists in reporting currently underdiagnosed osteoporotic vertebral fractures so that appropriate therapy can be initiated. KEY POINTS: • This spine segmentation algorithm automatically localised, identified, and segmented the vertebrae in MDCT images. • Osteoporotic vertebral fractures could be automatically detected using this prototype algorithm. • The prototype algorithm helps radiologists to report underdiagnosed osteoporotic vertebral fractures.