一个开源的nnU-net算法用于自动细分男性骨盆的MRI扫描,用于自适应性放射治疗
Ebbe Laugaard Lorenzen1,2, Bahar Celik1, Nis Sarup1
1Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark.
Frontiers in oncology
|November 29, 2023
概括
一个自动化的nnU-net框架为自适应性MRI导向放射治疗 (MRIgRT) 提供了准确和高效的器官细分,在速度和精度方面优于男性骨盆区域治疗的当前临床方法.
科学领域:
- 医疗成像医学成像
- 放射疗法是一种放射治疗.
- 人工智能的人工智能
背景情况:
- 适应性MRI指导放射治疗 (MRIgRT) 需要在MRI扫描上精确的器官和目标细分.
- 手动细分是艰苦和不一致的;可变形图像注册 (DIR) 与显著的解剖变化作斗争.
研究的目的:
- 开发和评估使用nnU-net框架用于男性骨盆MRIgRT的自动化细分方法.
- 将自动化方法的性能与基于DIR的当前临床工作流进行比较.
主要方法:
- 一个nnU-net框架在38名局部前列腺癌患者的76个扫描上受过训练.
- 该网络在30名患有局部前列腺,转移前列腺或膀癌的患者的60次扫描上进行了测试,使用1.5 T MRI-linac.
- 分区的准确性是使用子相似系数 (DSC),平均表面距离 (MSD) 和豪斯多夫距离 (HD) 来评估的.
主要成果:
- nnU-net实现了高细分精度,90%的轮显示DSC>0.9和86%的MSD<1毫米.
- 自动化工作流在大约1分钟内完成了细分,显著超过了基于DIR的轮转移.
- nnU-net表现出卓越的性能,特别是对于体积变化很大的器官.
结论:
- 一个自动化的nnU-net模型有效地对男性骨盆中的MRIgRT的器官和目标进行细分.
- 这种nnU-net方法在1.5TMRI-linc治疗的准确性和速度上超过了当前基于DIR的临床实践.
- 训练有素的网络适用于临床在线MRIgRT,该模型可作为开源提供.
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