Introduction Results Material & Methods Conclusions Computer Aided Diagnosis of Vertebral Fracture Using Appearance Models and Automatic Segmentation Roberts MG, Cootes TF, Pacheco E, Adams JE University of Manchester, UK Introduction Results Vertebral fractures are a strong indicator of osteoporosis. Quantitative morphometric assessment for vertebral fracture can be made from DXA images of the spine, but is insufficiently specific. We used detailed statistical models of the shape and appearance of vertebrae to develop more reliable quantitative classification methods1. These can be combined with an automatic segmentation. Spinal Region Appearance Classifier FPR (%) 3-height morphometry FPR (%) T7-T9 3.2% 21.6% T10-T12 4.7% 18.5% L1-L4 4.9% 10.0% False positive rates (FPR) (%) at 95% sensitivity for an appearance classifier compared with standard 3-height morphometry (manually segmented vertebrae) a b At 95% sensitivity the appearance classifier has an overall false positive rate (FPR) under 5% (manually segmented vertebrae), compared to 18% false positive rate with standard morphometry. This produces a sensitivity of 85% for grade 1 fractures, compared to 65% using height-based morphometry. When applied to automatically segmented vertebrae the overall sensitivities at 2.5, 5% FPR respectively are:78%,86%(appearance); 66%,75% morphometry). Fig b) shows a zoomed-in view of the points that are used to produce the shape models. T9 (severe fracture) and T10 (normal) are shown. Fig a) shows part of a DXA image, with a T12 fracture evident Material & Methods A fracture-rich dataset of 360 DXA images was used. The vertebral bodies were manually segmented from L4 to T7, and statistical models of vertebral shape and texture were derived. The shape and texture model parameters were combined to create vertebral appearance models. The vertebrae were visually assessed by two radiologists using the Algorithm Based Qualitative (ABQ) method2. There were 354 fractures and 158 other short vertebral height deformities identified. The shape and appearance models were re-fitted to each training image, and their resulting model parameters used to train linear discriminant classifiers, given the radiologists’ gold standard. Classifier performance on unseen data was assessed using leave-1-out train/test experiments, and ROC curves were derived. Classifier performance was evaluated using both the manually segmented vertebral outlines, and also automatically derived segmentations, obtained by using Active Appearance Models to automatically locate the vertebrae. References: 1) Roberts et al Acad Radiol 2007;14:1166-1178 2) Jiang et al Osteoporos Int 2004;15:887-896 Combined ROC curves for appearance classifier, and for 3-height morphometry. Results are shown for manually segmented vertebrae (Man Seg in Legend), and for an automatic segmentation (Auto Seg in Legend) obtained using Active Appearance Models. Conclusions The appearance classifier can distinguish between true vertebral fractures and other short vertebral height deformities more reliably than height-based methods. This technique has the potential to be used clinically, when combined with Active Appearance Model automatic segmentation.