研究一种基于深度学习的模型,用于测量大西洋轴心关节的X射线成像参数
Yuhua Wu1,2, Yuwen Zheng3, Jinping Zhu4
1Gansu University of Traditional Chinese Medicine, Lanzhou, China. 2687925983@qq.com.
概括
一个深度学习模型,SCNet,准确地从宫X射线测量大西洋轴突 (AAS) 参数. 该模型展示了稳定性和概括能力,在地标识别和参数测量方面显示出高精度.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 大西洋轴突突变 (AAS) 需要精确测量特定的放射性参数.
- 这些参数的手动测量可能耗时,并且受观察者之间的变化影响.
研究的目的:
- 开发和评估基于深度学习的SCNet模型,用于自动测量AAS相关的成像参数.
- 为了评估SCNet模型在宫开口视图X线图中的准确性和可靠性.
主要方法:
- 一个深度学习模型 (SCNet) 在1973年两家医院的宫开口放射图上进行了训练和验证.
- 该模型确定了横向亚特兰大-牙空间 (LADS),亚特兰大横向质量倾斜 (ALI),横向质量宽度 (LW) 和轴状过程偏差距 (ASDD) 的地标.
- 使用正确关键点百分比 (PCK),类内相关系数 (ICC) 和平均绝对误差 (MAE) 对放射科医生手动测量进行评价.
主要成果:
- 在内部和外部测试数据集上,SCNet在里程碑识别方面实现了高PCK (98-100%).
- 在内部数据集上,SCNet和手动测量LADS,ALI,LW和ASDD之间观察到强烈的相关性和一致性 (ICC=0.80-0.96,r=0.86-0.96).
- 在外部测试数据集中发现了类似的强相关性 (ICC=0.81-0.91,r=0.82-0.91),表明了强度和通用性.
结论:
- 该SCNet深度学习模型准确地识别了atlantoaxial地标,并在宫放射图中测量了AAS相关的参数.
- 该模型在独立的外部测试集上展示了强度和概括能力,满足了放射性标准.
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