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监督较弱的深度学习用于多模式MRI-TRUS注册:朝着协助前列腺活检指导
Jiabin Yu1,2,3,4, Binggang Xiao1, Jiayi Wang1
1Department of Artificial Intelligence, College of Information Engineering, China Jiliang University, Hangzhou, Zhejiang, China.
Digital health
|September 11, 2025
概括
这项研究引入了一个弱监督的学习框架,用于准确的磁共振成像 - 跨直肠超声波 (MRI-TRUS) 注册,以最小的标记数据改进前列腺癌检测. 该方法表现出强大的通用性和临床适用性,用于增强活检指导.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 前列腺癌的诊断方法 前列腺癌的诊断方法
背景情况:
- 精确的磁共振成像 (MRI) 和穿直肠超声波 (TRUS) 的记录对于有效的前列腺癌检测和活检指导至关重要.
- 目前的方法通常需要大量的标记数据,这限制了它们的广泛临床采用.
研究的目的:
- 开发和验证一个弱监督的学习框架,以实现强大的MRI-TRUS注册.
- 为了提高前列腺癌检测和活检的准确性,同时尽量减少手动注释的需要.
主要方法:
- 实施了两阶段的弱监督框架,包括用于前列腺细分的注意力增强的U-Net和用于图像对齐的残余增强注册网络 (RERN).
- 在公共和临床MRI-TRUS数据集上训练和评估模型,使用诸如子相似系数 (DSC) 和豪斯多夫距离 (HD95) 等指标.
主要成果:
- 分段模型实现了高精度 (DSC:MRI 0.9154,TRUS 0.9384) 和对临床数据的概括性.
- 注册模型表现出强大的性能,HD95为10.18毫米 (公共) 和11.18毫米 (临床),目标注册误差 (TRE) 低于8.64毫米.
- 临床验证显示,高级放射科医生的诊断完整性得到保护,诊断信心得到增强.
结论:
- 拟议的弱监督框架允许高精度MRI-TRUS注册,要求注释最小.
- 该方法确保了强大的通用性和临床适用性,为前列腺癌管理提供了宝贵的工具.
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