BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning
BMD-GAN: Bone mineral density estimation using x-ray image decomposition into projections of bone-segmented quantitative computed tomography using hierarchical learning
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DOI：
10.48550/arxiv.2207.03210
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发表时间：
2022-07
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影响因子：
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通讯作者：
Yidong Gu;Y. Otake;K. Uemura;M. Soufi;M. Takao;N. Sugano;Yoshinobu Sato
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作者：
Yidong Gu;Y. Otake;K. Uemura;M. Soufi;M. Takao;N. Sugano;Yoshinobu Sato
． 我们提出了一种方法来估计骨矿物质密度（BMD）从一个普通的x射线图像。双能x线吸收仪（DXA）和定量计算机断层扫描（QCT）对骨质疏松症的诊断具有较高的准确性；然而，这些模式需要特殊的设备和扫描协议。从x线图像测量骨密度提供了机会性筛查，这对早期诊断可能有用。以往直接学习x射线图像与BMD之间关系的方法，由于x射线图像的强度变化较大，需要较大的训练数据集才能达到较高的精度。因此，我们提出了一种使用QCT训练生成对抗网络（GAN）的方法，并将x射线图像分解为骨分割QCT的投影。提出的分层学习方法提高了小面积目标定量分解的鲁棒性和准确性。使用我们提出的方法（我们将其命名为BMD- gan）对200例骨关节炎患者进行评估，结果显示，预测的骨密度与dxa测量的实际骨密度之间的Pearson相关系数为0.888。除了不需要大规模的训练数据库之外，我们的方法的另一个优点是它可以扩展到其他解剖区域，例如椎骨和肋骨。
. We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. Measuring BMD from an x-ray image provides an opportunistic screening, which is potentially useful for early diagnosis. The previous methods that directly learn the relationship between x-ray images and BMD require a large training dataset to achieve high accuracy because of large intensity variations in the x-ray images. Therefore, we propose an approach using the QCT for training a generative adversarial network (GAN) and decomposing an x-ray image into a projection of bone-segmented QCT. The proposed hierarchical learning improved the robustness and accuracy of quantitatively decomposing a small-area target. The evaluation of 200 patients with osteoarthritis using the proposed method, which we named BMD-GAN, demonstrated a Pearson correlation coefficient of 0.888 between the predicted and ground truth DXA-measured BMD. Besides not requiring a large-scale training database, another advantage of our method is its extensibility to other anatomical areas, such as the vertebrae and rib bones.