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

Quantitative Vertebral Fracture Detection on DXA Images using Shape and Appearance Models. M.G. Roberts , T.F. Cootes, E. Pacheco, J.E. Adams. Imaging Science and Biomedical Engineering, University of Manchester, U.K. Contents. Clinical Background Appearance Models Classifier Training

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

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  1. Quantitative Vertebral Fracture Detection on DXA Images using Shape and Appearance Models M.G. Roberts, T.F. Cootes, E. Pacheco, J.E. Adams Imaging Science and Biomedical Engineering, University of Manchester, U.K.

  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

  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

  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

  8. Example Shape Fit T12 wedge fracture

  9. L2 Triplet Shape Modes 1-5 Derive shape models from manually annotated training images

  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

  23. For more information: martin.roberts@manchester.ac.uk www.isbe.man.ac.uk/~mgr/autospine.html This work was funded by the UK’s ARC (Arthritis Research Campaign) Earlier model development work was funded by a grant from the Central Manchester and Manchester Children’s University Hospitals NHS Endowment Trust.

  24. DIVA Tool Whole spine view Morphometry table + classification Zoom view User initialises solution by clicking on approximate centres of vertebrae Then the tool uses Active Appearance Model search to locate shape contours around each vertebra

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