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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www.isbe.man.ac.uk

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martin.roberts@man.ac.uk