通过使用3D-CT合成图像的深卷积神经网络在盆腔X射线中增强骨折诊断
Rashedur Rahman1, Naomi Yagi2, Keigo Hayashi3
1Graduate School of Engineering, University of Hyogo, 2167 Shosha, Himeji, 671-2201, Japan. rashed.riyadh14@gmail.com.
Scientific reports
|April 5, 2024
概括
一种使用CT扫描合成X射线的新两步转移学习方法,与标准方法相比,显著改善了从骨盆X射线诊断骨盆骨折的诊断.
科学领域:
- 医疗成像医学成像
- 放射学中的人工智能
- 骨科诊断 骨科诊断 骨科诊断
背景情况:
- 骨盆骨折由于复杂的解剖学而带来诊断挑战.
- 准确和及时的诊断对于患者的治疗结果至关重要.
- 当前的深度学习方法经常使用ImageNet,它与盆腔X射线 (PXR) 图像特征有所不同.
研究的目的:
- 开发和评估一种新的两步转移学习方法,以改善PXRs中的骨盆骨折检测.
- 将拟议的方法与传统的基于ImageNet的转移学习进行比较.
主要方法:
- 一个深层卷积神经网络 (DCNN) 通过通过数字重建射线图 (DRR) 从3DCT扫描生成的合成PXRs进行训练.
- 随后,DCNN的分类层与实际的PXR图像进行了微调.
- 通过将基于DRR的方法与基于ImageNet的转移学习方法进行比较来评估性能.
主要成果:
- 提出的基于DRR的转移学习方法在诊断骨盆骨折方面取得了卓越的表现.
- 可见骨折的接收器运行特征曲线 (AUROC) 下的面积为0.9327 (基于DRR) 和0.8908 (基于ImageNet).
- 隐形骨折的AUROC为0.8014 (基于DRR) 与0.7308 (基于ImageNet) 相比.
结论:
- 基于DRR的两步转移学习方法显著提高了从PXRs诊断骨盆骨折的准确性.
- 从CT数据中合成PXR图像为此任务提供了一个比自然RGB图像更适合的预训练数据集.
- 这种方法为骨盆骨折的放射诊断提供了有前途的进展.
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