在CBCT指导放射治疗中,完全无监督的异常检测框架的概念证明
Dishane C Luximon1, Moritz Ritter1,2, Rachel Petragallo1,3
1University of California, Los Angeles, California, USA.
Medical physics
|August 9, 2025
概括
该iGuARD框架使用无监督方法来检测放射治疗成像中的异常,通过标记偏差进行审查来提高患者的安全性和治疗质量.
科学领域:
- 放射治疗图像指导指导 放射治疗图像指导
- 医学成像分析分析 医学成像分析
- 机器学习在医疗保健中的应用
背景情况:
- 圆束计算机断层扫描 (CBCT) 放射治疗图像指导中的异常,例如错位和软组织变异,可能导致治疗偏差.
- 手动审查常规对齐图像是低效的,容易产生偏差,并且在检测罕见事件方面无效.
研究的目的:
- 提出一个无监督的图像引导异常识别和检测 (iGuARD) 框架.
- 使用变异自编码器 (VAE) 进行CBCT inpainting以突出显示异常以供人类审查.
主要方法:
- 开发了一个iGuARD框架,使用训练有素的VAE用于基于模拟CT的CBCT扫描.
- 通过测量实际和染色CBCT之间的图像相似性来检测异常,较高的得分表明异常.
- 使用诸如平均平方误差,相互信息和结构相似性等指标评估性能,并进行维度减小和k-means聚类以获得异常得分.
主要成果:
- 在未见的测试数据集上,iGuARD框架在95%的灵敏度下实现了74.1%的特异性,超过了传统方法 (59.9%).
- 在完整的数据集中,iGuARD识别了所有已知的事件 (100%的灵敏度),具有93.0%的特异性,而传统方法的特异性为77.9%.
- 异常病例通常表现为软组织变异和CBCT图像质量差.
结论:
- 该iGuARD框架自动识别患者设置CBCT扫描和治疗分数偏离正常.
- 这种工具可以提高质量保证检查的效率,并可能改善患者安全和治疗结果.
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