基于深度学习的自动细分结果对图像引导放射治疗中的每日千伏,兆伏和圆束CT图像的比较
Zhixing Wang1, Chengyu Shi1, Carson Wong1
1Department of Radiation Oncology, City of Hope, Duarte, CA, USA.
Technology in cancer research & treatment
|May 21, 2025
概括
对于放射治疗的深度学习自分段显示,与kV圆光束CT (kV-CBCT) 或大电压CT (MVCT) 相比,千伏CT (kV-CBCT) 成像显示最佳结果. 手动轮调整对于所有模式仍然是必不可少的.
科学领域:
- 放射治疗和医学成像技术
- 医疗保健中的人工智能
- 计算解剖学的计算解剖学
背景情况:
- 对处于危险的器官进行准确的自我细分对于图像导向放射治疗 (IGRT) 至关重要.
- 深度学习模型为自动化轮提供了潜力,但性能在成像模式上有所不同.
- 在不同的CT成像技术上评估自分段精度对于临床实施至关重要.
研究的目的:
- 评估深度学习自动细分模型在各种CT成像模式中的性能.
- 将自动分割轮的精度与千伏CT (kVCT),kV圆束CT (kV-CBCT) 和兆电压CT (MVCT) 的手动划分进行比较.
- 确定IGRT中基于深度学习的自动细分的最佳成像模式.
主要方法:
- 幻影研究是为了比较图像质量而进行的.
- 从kVCT,kV-CBCT和MVCT模式中对60名患者的每日CT图像进行回顾性分析.
- 深度学习模型 (卷积神经网络) 用于对有风险的器官进行自动细分.
- 包括子相似系数 (DSC) 和豪斯多夫距离在内的定量指标被用于与手动轮进行比较.
主要成果:
- 对于大多数主要器官,kV-CBCT和MVCT相比,kV-CBCT上的自动细分证明了与手动轮的统计学上显著的优越一致.
- 在盆腔病例中,kVCT实现了肠道平均DSC为0.84±0.05,明显高于kV-CBCT (0.35±0.23) 和MVCT (0.48±0.27).
- 在胸部病例中,kVCT给食道带来了0.63±0.16的平均DSC,优于kV-CBCT (0.18±0.13) 和MVCT (0.22±0.08).
结论:
- 深度学习自动细分模型在kVCT图像中表现最好,与手动划分的一致性比kV-CBCT或MVCT更大.
- 尽管取得了进展,但在所有评估的成像模式中,手动轮校正仍然是必要的,特别是在具有低对比度的器官中.
- 这些发现凸显了深度学习对适应性放射治疗应用的自我细分的当前能力和局限性.
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