用深度学习与不确定性估计进行MRI导向前列腺放射治疗的临床目标体积划分质量保证
Hang Min1, Jason Dowling2, Michael G Jameson3
1CSIRO Australian e-Health Research Centre, Herston, Queensland, Australia; Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia; South Western Clinical Campuses, University of New South Wales, Australia.
概括
本研究引入了深度学习 (DL) 框架,用于MRI导向前列腺放射治疗中临床目标体积 (CTV) 划分的自动质量保证 (QA),提高精度和速度.
科学领域:
- 医学物理 医学物理
- 放射治疗技术 放射治疗技术
- 人工智能在医学中的应用
背景情况:
- 目前用于放射治疗计划的自动质量保证 (QA) 主要集中在CT扫描上.
- 对前列腺癌的MRI指导放射治疗越来越多地使用,需要MRI特定的自动QA解决方案.
- 准确的临床目标体积 (CTV) 划定对于有效的前列腺癌治疗至关重要.
研究的目的:
- 提出和评估一种基于深度学习 (DL) 的新型框架,用于MRI导向前列腺放射治疗中临床目标体积 (CTV) 划分的自动质量保证 (QA).
- 在前列腺癌治疗的背景下,解决对MRI特异性QA方法的需求.
- 开发一种工具,可以帮助审查前列腺CTV划分,特别是在多中心临床试验中.
主要方法:
- 一个3D dropblock ResUnet++ (DB-ResUnet++) 模型被用来生成使用蒙特卡洛dropout.out的多重细分预测.
- 从这些预测中计算出平均划分和不确定性区域.
- 一个物流回归 (LR) 分类器评估了与网络输出相对应的手动划分,将其分类为通过或不一致.
主要成果:
- 拟议的框架实现了0.92.9的接收器运行曲线下的面积 (AUROC).
- 获得了0.92的真实阳性率 (TPR) 和0.09的假阳性率 (FPR).
- 与以前的方法相比,每个划线的平均处理时间显著减少到1.3分钟,假阳性结果较少.
结论:
- 本文介绍了首个使用深度学习和不确定性估计的自动划分QA工具,专门用于MRI引导的前列腺放射治疗.
- 开发的框架在前列腺CTV划分QA的准确性和处理速度方面表现出很高的性能.
- 该工具有可能提高多中心临床试验中前列腺CTV划分的审查过程,提高一致性和效率.
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