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通过使用空间相关器官分割的联合学习来改善胸部器官划分的旋转强度的研究:基于U-net的比较
Jie Zhang1,2, Yiwei Yang1,2, Min Fang1,2
1The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
Journal of applied clinical medical physics
|July 20, 2023
概括
空间相关器官细分 (SCOS) 的联合学习增强了胸部器官划分的强度,防止旋转. 这种方法由于其效率,对临床使用有希望.
科学领域:
- 医疗成像医学成像
- 辐射疗法 辐射疗法
- 计算解剖学的计算解剖学
背景情况:
- 胸部器官的准确划线对于放射治疗计划至关重要.
- 患者定位的旋转变化可以显著影响细分精度.
- 现有的方法可能在医学图像细分中与旋转稳定性作斗争.
研究的目的:
- 调查空间相关器官细分 (SCOS) 的有效性,以改善胸部器官划分中的旋转强度.
- 为了评估一个U-net-like模型,将SCOS (SCOS-net) 结合在旋转下对细分精度进行评估.
主要方法:
- 在U-net类架构 (SCOS-net) 中实现了SCOS.
- 在267名胸部瘤患者的数据集上进行评估,包括旋转和非旋转扫描.
- 将SCOS-net与单任务学习,其他多任务学习方法以及使用Dice和Hausdorff距离指标的旋转增强进行了比较.
主要成果:
- 在没有增强的情况下,SCOS-net显示了肺和脊髓的优越细分.
- 在心脏细分方面取得了可比的结果.
- 在与增强相结合时,在特定情况下显示出更好的性能.
结论:
- 空间相关器官细分 (SCOS) 有效地提高了胸部器官划分的旋转强度.
- 在SCOS方法是计算效率高,并具有低的网络容量.
- 该方法显示了在放射治疗中临床应用的巨大潜力.
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