不同计算机断层扫描协议和供应商之间基于生成对抗网络的图像转换:对量化间歇性肺部疾病区域疾病模式的准确性和可变性的影响
Hye Jeon Hwang1, Hyunjong Kim2, Joon Beom Seo3
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Korean journal of radiology
|July 27, 2023
概括
生成对抗性网络 (GAN) 改善了计算机断层扫描 (CT) 转换以量化间歇性肺病 (ILD). 这提高了准确性,并减少了CT扫描上ILD深度学习分析的可变性.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 在CT扫描中量化间歇性肺病 (ILD) 对诊断和管理至关重要.
- CT扫描参数和制造商的变化可能会影响自动ILD量化的准确性.
- 基于深度学习的软件对ILD量化有前途,但对图像采集变化敏感.
研究的目的:
- 评估可路由生成对抗网络 (RouteGAN) 对于CT图像转换的有效性.
- 评估RouteGAN转换是否提高了基于深度学习的ILD量化准确性和减少了ILD量化的变化.
- 为了在不同的扫描仪和参数中标准化CT图像,以便更可靠的ILD分析.
主要方法:
- 150名ILD患者的CT图像使用来自四家制造商的扫描仪采集,剂量和内核各不相同.
- 使用RouteGAN将来自不同采集条件的CT图像转换为标准化的目标风格.
- 深度学习软件 (Aview) 在原始和转换的CT图像上量化了ILD;使用子相似系数 (DSC),像素智能指标和放射科医生评分来评估准确性.
主要成果:
- CT转换显著改善了总异常 (P < 0.002) 的重叠精度指标 (DSC,像素智能回忆,精度).
- 纤维化,蜂和网膜的DSC在转化后显著增加 (P < 0.002).
- 放射科医生对量化准确度的评分在转换的CT图像上显著更高,变化较小 (P < 0.001).
结论:
- 使用RouteGAN的CT图像转换有效地标准化了使用不同参数和制造商获得的图像.
- 这种转换过程提高了准确性,并减少了基于深度学习的ILD量化中的变化.
- 基于RouteGAN的CT转换是提高自动ILD评估可靠性的宝贵工具.
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