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Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance Martin Roberts and Tim Cootes and Judith Adams

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Presentation on theme: "Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance Martin Roberts and Tim Cootes and Judith Adams"— Presentation transcript:

1 Vertebral shape: automatic measurement by DXA using overlapping statistical models of appearance Martin Roberts and Tim Cootes and Judith Adams 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 Osteoporosis - statistics 40% of middle-aged women in Europe affected 200,000 osteoporotic fractures per year in the UK –Half of these are vertebral Given one vertebral fracture: – the risk of a future hip fracture is doubled (or even tripled) –the risk of any subsequent vertebral fracture increases five-fold

6 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

7 Disadvantages of DXA Definition and resolution much poorer than conventional radiography –But now improved to 0.35mm line pair Images suffer from high noise or clutter from ribs & soft tissue Upper vertebrae (T4-T6) often poorly visualised –But these have lower fracture incidence –Arm positioning can also help

8 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

9 Comparisons with spinal radiography Good concordance between visual DXA and visual XR –Ferrar et al JBMR 2003 Good concordance between morphometric methods for DXA and Radiography –Rea Ost Int 1999, Ferrar JBMR 2000 Majority of discrepancy over Grade 1 mild fractures/deformities or T6-T4 Useful pre-screen to avoid higher radiation spinal radiographs

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11 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 Apr

12 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

13 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

14 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)

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

16 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

17 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

18 Automatic Search Accuracy Results Vertebra Status Median (mm) 90%-ile (mm) %Pts Error<2 Normal % Fractured or Deformed % Search Errors (per vertebra pooling T7-L4) Some under-training for fractures – causes long tail

19 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

20 Acknowledgements Acknowledge assistance of: –Bone Metabolism Group, University of Sheffield R Eastell, L Ferrar, G Jiang

21 For more… FOR MORE INFO...


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