论文标题
胸部X射线图像的腰骨矿物质密度估计：解剖学意识到专注的多ROI建模
Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling
论文作者
论文摘要
骨质疏松症是一种常见的慢性代谢骨疾病，由于骨矿物质密度（BMD）检查的机会有限，例如通过双能X射线吸收法（DXA）。本文提出了一种预测胸部X射线（CXR）BMD的方法，胸部X射线（CXR）是最常见和低成本的医学成像检查之一。我们的方法首先自动检测局部CXR骨结构的兴趣区域（ROI）。然后开发了具有变压器编码器的多动物深模型，以利用胸部X射线图像中的局部和全局信息，以进行准确的BMD估计。我们的方法对13719 CXR患者病例进行了地面真相BMD的评估，该病例由黄金标准DXA测量。该模型预测BMD与地面真相具有很强的相关性（Pearson相关系数为0.894在腰椎1上）。当应用于骨质疏松症筛查时，它会达到高分类性能（平均AUC为0.968）。作为使用CXR扫描来预测BMD的首次努力，拟议的算法具有早期骨质疏松症筛查和公共卫生促进的强大潜力。
Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g. via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. Our method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential for early osteoporosis screening and public health promotion.
