vertebral shape: automatic measurement by dxa using overlapping statistical models of appearance
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Vertebral shape: automatic Vertebral shape: automatic measurement by DXA using measurement by DXA using overlapping statistical models of overlapping statistical models of appearance appearance
Martin Roberts and Tim Cootes and Judith Adamsmartin.roberts@man.ac.uk
Imaging Science and Biomedical Engineering,
University of Manchester, UK
ContentsContents
Osteoporosis - Background DXA vs Conventional Radiography Fracture Classification Our aims in automating vertebral DXA Automatic Location Method Results for Vertebral Morphometry Accuracy Conclusions
OsteoporosisOsteoporosis
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
Osteoporosis – Vertebral FracturesOsteoporosis – 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
Advantages of DXAAdvantages 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
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
Classification methodsClassification 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
Our AimsOur 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
Automatic LocationAutomatic 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
Appearance ModelsAppearance 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)
Example AAM fit to DXA image
User initialises by clicking 3 points at bottom, middle, top (L4, T12, T7).
DatasetDataset
184 DXA images80 images contain fractures
– 137 vertebral fracturesAlso a bias towards obese patients
– So often high noise in lumbarSome other pathologies present
– Disk disease, large osteophytesSo challenging dataset
ExperimentsExperiments
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
Automatic Search Accuracy ResultsAutomatic Search Accuracy Results
Vertebra
Status
Median
(mm)
90%-ile
(mm)
%Pts Error<2
Normal 0.73 1.20 98.2%
Fractured or Deformed
0.94 2.82 84.6%
Search Errors (per vertebra pooling T7-L4)
Some under-training for fractures – causes long tail
Conclusions 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
AcknowledgementsAcknowledgements
Acknowledge assistance of:– Bone Metabolism Group, University of
SheffieldR Eastell, L Ferrar, G Jiang
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