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Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance

Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance. Martin Roberts and Tim Cootes and Judith Adams martin.roberts@man.ac.uk. Imaging Science and Biomedical Engineering, University of Manchester, UK. Contents. Osteoporosis - Background

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Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance

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  1. Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance Martin Roberts and Tim Cootes and Judith Adams martin.roberts@man.ac.uk Imaging Science and Biomedical Engineering, University of Manchester, UK

  2. Contents • Osteoporosis - Background • DXA vs Conventional Radiography • Fracture Classification • Our aims in automating vertebral DXA • Automatic Location Method • Results for Vertebral Morphometry Accuracy • Conclusions

  3. Osteoporosis • Disease characterised by: • Low bone mass or • 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. Osteoporosis – Vertebral Fractures • A vertebral fracture indicates increased risk of future fractures: • the risk of a future hip fracture is doubled (or even tripled) • the risk of any subsequent vertebral fracture increases five-fold • A very important diagnosis for radiologists to make • Incident vertebral fractures used in clinical trials • To assess the efficacy of osteoporosis therapies

  5. Advantages of DXA • Very Low Radiation Dose • 1/100 of spinal radiographs • Little or no projective effects: • “Bean Can” effects unusual • Constant scaling across the image • Whole spine on single image • C-arms offer ease of patient positioning • Convenient as supplement to BMD scan

  6. Example DXA image lateral view of spine Disadvantages Very low dose but noisy Poorer resolution than radiography (0.35mm vs 0.1mm) Above T7 shoulder-blades can cause poor imaging of T6-T4

  7. Classification methods • Quantitative morphometry - height ratios • Much shape information discarded • (3 heights) • Texture clues unused • e.g. wider texture band around an endplate collapse • So visual XR or Genant semi-quantitative more favoured • But subjectivity still a problem for mild fractures • Mild deformities may be mis-classed as fractures • Algorithm-based qualitative identification (ABQ) • Comparison of methods for the visual identification of prevalent vertebral fracture in osteoporosis.Jiang G, Eastell R, Barrington NA, Ferrar L.Osteoporos Int. 2004 Apr

  8. Our Aims • Automate the location of vertebral bodies • Fit full contour (not just 6 points) • Then use quantitative classifiers but • Use ALL shape information • And texture around shape

  9. Automatic Location • User clicks on bottom, top and middle vertebrae • Start at mean shape through these 3 points • Fit a sequence of linked appearance models • Overlapping triplets • E.g (L4/L3/L2), and (L3/L2/L1) etc • Overlaps give helpful linking constraints • Sequence Order is dynamically adjusted based on local quality of fit • High noise or poor fit regions deferred

  10. Appearance Models • Statistical Model of both shape and surrounding texture • Learned from a training set of manually annotated images • Good robustness to noise • shapes constrained by training set • But need large training set to fit to extreme pathologies • (e.g. grade 3 fractures)

  11. Example AAM fit to DXA image User initialises by clicking 3 points at bottom, middle, top (L4, T12, T7).

  12. Dataset • 184 DXA images • 80 images contain fractures • 137 vertebral fractures • Also a bias towards obese patients • So often high noise in lumbar • Some other pathologies present • Disk disease, large osteophytes • So challenging dataset

  13. Experiments • Repeated Miss-4-out tests • 180 image Training Set and 4 Test Set partition • 10 replications with emulated user-supplied initialisation (Gaussian errors) • Manual annotations as Gold Standard • Mean Abs Point-to-Curve Error per vertebra • Percentage number of points within 2mm also calculated

  14. Automatic Search Accuracy Results Search Errors (per vertebra pooling T7-L4) Some under-training for fractures – causes long tail

  15. Conclusions • Good automatic accuracy on normal vertebrae • Promising accuracies on fractured vertebrae • Need to extend training set • Vertebral shapes can be reliably located on DXA with only minimal manual intervention • This allows a new generation of quantitative classification methods • Could extend to digitised radiographs

  16. Acknowledgements • Acknowledge assistance of: • Bone Metabolism Group, University of Sheffield R Eastell, L Ferrar, G Jiang

  17. For more… www.isbe.man.ac.uk FOR MORE INFO... martin.roberts@man.ac.uk

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