1 / 18

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

soren
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related