Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation.

SUMMARY

Morphometric methods of vertebral fracture diagnosis lack specificity. We used detailed shape and image texture model parameters to improve the specificity of quantitative fracture identification. Two radiologists visually classified all vertebrae for system training and evaluation. The vertebral endplates were located by a semi-automatic segmentation method to obtain classifier inputs.

INTRODUCTION

Vertebral fractures are common osteoporotic fractures, but current quantitative detection methods (morphometry) lack specificity. We used detailed shape and texture information to develop more specific quantitative classifiers of vertebral fracture to improve the objectivity of vertebral fracture diagnosis. These classifiers require a detailed segmentation of the vertebral endplate, and so we investigated the use of semi-automated segmentation methods as part of the diagnosis.

METHODS

The vertebrae in a training set of 360 dual energy X-ray absorptiometry images were manually segmented. The shape and image texture of vertebrae were statistically modelled using Appearance Models. The vertebrae were given a gold standard classification by two radiologists. Linear discriminant classifiers to detect fractures were trained on the vertebral appearance model parameters. Classifier performance was evaluated by cross-validation for manual and semi-automatic segmentations, the latter derived using Active Appearance Models (AAM). Results were compared with a morphometric algorithm using the signs test.

RESULTS

With manual segmentation, the false positive rates (FPR) at 95% sensitivity were: 5% (appearance) and 18% (morphometry). With semi-automatic segmentations the sensitivities at 5% FPR were: 88% (appearance) and 79% (morphometry).

CONCLUSION

Specificity and sensitivity are improved by using an appearance-based classifier compared to standard height ratio morphometry. An overall sensitivity loss of 7% occurs (at 95% specificity) when using a semi-automatic (AAM) segmentation compared to expert annotation, due to segmentation error. However, the classifier sensitivity is still adequate for a computer-assisted diagnosis system for vertebral fracture, especially if used in a triage approach.