深度学习模型的不确定性对基于MRI的前列腺癌放射治疗的手动校正的影响
Viktor Rogowski1,2, Angelica Svalkvist3,4, Matteo Maspero5,6
1Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.
Journal of applied clinical medical physics
|August 24, 2025
概括
深度学习 (DL) 不确定性地图提高了放射瘤学家在前列腺放射治疗细分方面的效率和信心. 这些地图的展示减少了不确定性较低的地区的编辑,提高了临床工作流程.
科学领域:
- 放射治疗
- 医学成像
- 人工智能
背景情况:
- 深度学习 (DL) 器官细分在放射治疗中至关重要.
- 来自DL模型的Voxel-wise不确定性图在临床上未得到充分利用.
研究的目的:
- 评估DL产生的不确定性地图对放射瘤学家的影响.
- 评估前列腺放射治疗中DL自分化的手动校正的影响.
主要方法:
- 在434例前列腺CTV和直肠细分中训练nnUNet DL模型.
- 使用SoftMax标准偏差计算的voxel-wise不确定性
- 四位瘤学家对DL细分进行了评分,并记录了编辑时间.
主要成果:
- DL模型实现了高分段精度 (Dice 0.97-0.99).
- 在三个瘤学家中,不确定性地图减少了14-33%的细分时间.
- 在使用地图的低不确定性区域 (0.0-0.1) 编辑较少.
结论:
- DL不确定性地图影响瘤学家的决策和信心.
- 低不确定性地区对DL预测的依赖性增加.
- 不确定性地图提高了放射治疗规划的效率和临床效用.
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