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两种深度学习模型的可解释性信息基准测试,用于MR引导的自适应性放射治疗中风险器官细分

H Sekkat¹, A Khallouqi², Y Hammouga³

¹Laboratory of Sciences and Engineering of Biomedicals, Biophysics and Health, Higher Institute of Health Sciences, Hassan 1st University, Settat, Morocco; Higher Institute of Nursing Professions and Health Techniques, Rabat, Morocco.

Journal of medical imaging and radiation sciences

|February 14, 2026

概括

这项研究发现,U-Net和Residual U-Net深度学习模型在MRI扫描中提供了可比且可靠的性能,用于在MRI扫描中划分有风险的胃肠道器官. 它们的可解释性和稳定性支持在MR引导的适应性放射治疗中潜在的应用.

科学领域:

医学成像和放射治疗.
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:

用于MR导向适应性放射治疗 (MRgRT) 的胃肠道危险器官 (OAR) 的手动细分是耗时且可变的.
深度学习模型提供了自动细分的潜力,但需要验证准确性,可解释性和可靠性.

研究的目的:

使用MRI对腹部OAR细分进行U-Net和剩余U-Net (ResUNet) 的比较.
用Grad-CAM分析量化评估这些深度学习模型的可解释性.

主要方法:

在使用5倍交叉验证的腹部MRI数据上训练和评估U-Net和ResUNet.
评估了使用子相似系数 (DSC),十字路口 (IoU) 和豪斯多夫距离 (HD95) 的细分性能.
使用Grad-CAM激活地图和本地化指标量化可解释性.

主要成果:

无论是U-Net还是ResUNet,都在肠道器官之间实现了可比的细分性能.
在Grad-CAM分析中,两种模型的注意力模式和定位精度都相似.
不确定性分析表明,U-Net和ResUNet的稳定性可比,并限制了最坏情况下的行为.

结论:

在腹部OAR细分方面,U-Net和ResUNet表现出稳定且可解释的性能.
这些深度学习模型显示了可靠地集成到MR引导的自适应性放射治疗工作流程中的潜力.
量化可解释性指标支持临床信任和采用这些人工智能模型.

关键词:

用MR指导的放射治疗 (MRgRT)磁力共振成像细分磁力共振成像细分有风险的器官 (OARs)在 Resunet 上重新启动.在U-net中,U-net是指U-net网络.

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