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Detection of Degenerative Change in Lateral Projection Cervical Spine X-ray Images

机译:侧向投影颈椎X射线图像的退行性变化的检测

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Degenerative changes to the cervical spine can be accompanied by neck pain, which can result from narrowing of the intervertebral disc space and growth of osteophytes. In a lateral x-ray image of the cervical spine, degenerative changes are characterized by vertebral bodies that have indistinct boundaries and limited spacing between vertebrae. In this paper, we present a machine learning approach to detect and localize degenerative changes in lateral x-ray images of the cervical spine. Starting from a user-supplied set of points in the center of each vertebral body, we fit a central spline, from which a region of interest is extracted and image features are computed. A Random Forest classifier labels regions as degenerative change or normal. Leave-one-out cross-validation studies performed on a dataset of 103 patients demonstrates performance of above 95% accuracy.
机译:颈椎的退行性变化可伴有颈部疼痛,这可能是由于椎间盘间隙变窄和骨赘的生长所致。在颈椎的侧向X射线图像中,退行性改变的特征是椎体边界不明确,椎骨之间的间距有限。在本文中,我们提出了一种机器学习方法来检测和定位颈椎侧面X射线图像中的退行性变化。从每个椎体中心的用户提供的一组点开始,我们拟合一个中心样条,从中提取感兴趣区域并计算图像特征。随机森林分类器将区域标记为退化性变化或正常。对103位患者的数据集进行的留一法交叉验证研究表明,其准确率超过95%。

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