Detection of sclerotic bone metastases in the spine using watershed algorithm and graph cut

Abstract

The early detection of bone metastases is important for determining the prognosis and treatment of a patient. We developed a CAD system which detects sclerotic bone metastases in the spine on CT images. After the spine is segmented from the image, a watershed algorithm detects lesion candidates. The over-segmentation problem of the watershed algorithm is addressed by the novel incorporation of a graph-cuts driven merger. 30 quantitative features for each detection are computed to train a support vector machine (SVM) classifier. The classifier was trained on 12 clinical cases and tested on 10 independent clinical cases. Ground truth lesions were manually segmented by an expert. The system prior to classification detected 87% (72/83) of the manually segmented lesions with volume greater than 300 mm 3 . On the independent test set, the sensitivity was 71.2% (95% confidence interval (63.1%, 77.3%)) with 8.8 false positives per case.