基于SE-DenseVoxNet的骨小梁模量预测方法

      Prediction Method of Elastic Modulus of Trabecular Bone Based on SE-DenseVoxNet

      • 摘要: 骨质疏松症是常见的代谢性疾病之一,容易引发骨质疏松性骨折。准确预测骨的生物力学性质对于骨质疏松性症的早期防治与诊断具有重要的意义。目前临床上采用骨密度测量作为评估骨强度、诊断骨质疏松症的金标准,但是研究表明骨密度只能解释60%~70%的骨强度变化,骨小梁微结构是影响骨强度的重要因素。为了建立骨小梁微结构与骨强度之间的联系,提出了一种基于SE-DenseVoxNet的骨小梁模量预测方法,该方法以骨小梁的三维二值图像作为输入,对其z轴方向的弹性模量进行预测。实验表明,该方法的预测结果与样本真值之间的误差和偏倚较小,具有较好的一致性。

         

        Abstract: Osteoporosis is one of the common metabolic diseases, which can easily lead to osteoporotic fractures. Accurate prediction of bone biomechanical properties is of great significance for the early prevention and diagnosis of osteoporosis. Bone mineral density measurement is currently used clinically as the gold standard for assessing bone strength and diagnosing osteoporosis, but studies have shown that bone mineral density can only explain 60% to 70% of bone strength changes, and trabecular bone microstructure is an important factor affecting bone strength. In order to establish the connection between trabecular bone microstructure and bone strength, this paper proposes a prediction method of trabecular bone modulus based on SE-DenseVoxNet. This method takes three-dimensional binary images of trabecular bone as input and predicts its elastic modulus in the z-axis direction. Experiments show that the error and bias between the predicted value of the method and the true value of the sample are small and have good consistency.

         

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