M.G. Roberts, T.F. Cootes, E. Pacheco, J.E. Adams

2. Contents Clinical Background Appearance Models Classifier Training ROC curves Conclusions

3. Osteoporosis Disease characterised by: Low bone mass and deterioration in trabecular structure Common Disease � affects up to 40% of post-menopausal women Causes fractures of hip, vertebrae, wrist Vertebral Fractures Most common osteoporotic fracture Occur in younger patients, so provide early diagnosis

4. Classification The Genant method has become a kind of de facto gold standard for assessing vertebral fractures, but has problems with subjectivity, and can confuse osteoporotic fracture with short vertebral height due to other reasons. Note the severely collapsed grade 3 shapes are the ones for which we are trying to improve segmentation accuracy. These lie in the tails of the shape distribution. The Genant method has become a kind of de facto gold standard for assessing vertebral fractures, but has problems with subjectivity, and can confuse osteoporotic fracture with short vertebral height due to other reasons. Note the severely collapsed grade 3 shapes are the ones for which we are trying to improve segmentation accuracy. These lie in the tails of the shape distribution.

5. Limitations of current methods Morphometric Methods not reliable Use of 3 heights loses too much subtle shape information? No texture clues used (e.g. signs of collapsed endplate) But expert assessment has subjectivity problems Apparently widely varying fracture incidence Shortage of radiologists for expert assessment Availability of DXA Scanners in GP surgeries

6. Our Aims Automate the location of vertebrae Fit full contour (not just 6 points) Then use quantitative classifiers Use ALL shape information And texture around shape However we are still addressing the reliabilty of the (semi)automatic locationHowever we are still addressing the reliabilty of the (semi)automatic location

7. DXA Images Very Low Radiation Dose Little or no projective effects: Tilting �Bean Can� effects unusual Constant scaling across the image Whole spine on single image C-arms offer ease of patient positioning No apparent tilting from the divergent beamNo apparent tilting from the divergent beam

8. Example Shape Fit

9. L2 Triplet Shape Modes 1-5

10. Appearance Models Combine Shape with Texture Sample image texture around/within shape Build texture model using PCA Combine shape and texture parameters Perform a tertiary PCA on combined vectors As shape/texture correlated This gives appearance model Appearance parameters determine both shape and texture

11. L2 Triplet Appearance Modes 1-3

12. Appearance Model Form Single vertebrae Models local edge structure in a region around the endplate

13. Classification Method Train Shape and Appearance Models Nearby Vertebrae are pooled T7-T9 T10-T12 L1-L4 Refit Models to training images Record shape and appearance model parameters With fracture status Hence train linear discriminants Tried both shape and appearance parameters Used 3 standard height ratios as baseline comparison

14. Dataset 360 DXA Images 343 Fractures 97 Mild (Grade 1) 141 Moderate (Grade 2) 105 Severe (Grade 3) 187 non-fracture deformities Classified using ABQ method 2 radiologist consensus

15. Lumbar Spine ROC curves

16. T10-T12 ROC curves

17. T7-T9 ROC Curves

18. Grade 1 Fractures Combined

19. Grade 2 Fractures

20. FPR at 95% sensitivity

21. FPR on Grade 1 Fractures at 85% sensitivity

22. Conclusions Reliable quantitative classification on appearance model parameters 92% specificity at 95% sensitivity vs 79% specificity for standard morphometry Potential for clinical diagnosis tool (CAD) And use in clinical trials

24. DIVA Tool