...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>European radiology >Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening
【24h】

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening

机译：使用低剂量胸部计算断层扫描扫描的自动机会骨质疏松症筛查，用于肺癌筛查

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Objective Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using low-dose chest computed tomography (LDCT) scans obtained for lung cancer screening. Methods First, a deep learning model was trained and tested with 200 annotated LDCT scans to segment and label all vertebral bodies (VBs). Then, the mean CT numbers of the trabecular area of target VBs were obtained based on the segmentation mask through geometric operations. Finally, a linear function was built to map the trabecular CT numbers of target VBs to their BMDs collected from approved software used for osteoporosis diagnosis. The diagnostic performance of the developed system was evaluated using an independent dataset of 374 LDCT scans with standard BMDs and osteoporosis diagnosis. Results Our deep learning model achieved a mean Dice coefficient of 86.6% for VB segmentation and 97.5% accuracy for VB labeling. Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964-0.968 and mean errors of 2.2-4.0 mg/cm(3). The area under the curve (AUC) was 0.927 for detecting osteoporosis and 0.942 for distinguishing low BMD. Conclusion The proposed deep learning-based system demonstrated the potential to automatically perform opportunistic osteoporosis screening using LDCT scans obtained for lung cancer screening.
机译：目标骨质疏松症是一种普遍和可治疗的病症，但它仍然是未结算的。在这项研究中，开发了一种基于深度学习的系统，以自动测量使用低剂量胸部计算断层扫描（LDCT）扫描的机会主义骨质疏松筛查的骨矿物密度（BMD）。方法首先，培训深层学习模型，并用200个注释的LDCT扫描进行培训，并将所有椎体（VBS）扫描和标记。然后，通过几何操作基于分割掩模获得目标VBS的平尖区域的平均CT数。最后，建立了线性函数，以将目标VB的小梁CT数映射到从用于骨质疏松症诊断的批准软件收集的BMD。使用374 LDCT扫描的独立数据集进行评估开发系统的诊断性能，标准BMDS和骨质疏松症诊断。结果我们的深度学习模式实现了VB分段的平均骰子系数86.6％，VB标签的精度为97.5％。线回归和平坦 - 奥特曼分析在预测的BMD和地面真理之间显示出良好的一致性，相关系数为0.964-0.968，平均误差为2.2-4.0mg / cm（3）。曲线（AUC）下的区域为0.927，用于检测骨质疏松症和0.942，以区分低BMD。结论拟议的基于深度学习的体系证明了使用为肺癌筛查获得的LDCT扫描自动进行机会主义骨质疏松筛查的可能性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号