图像工厂:一种用于合成具有解剖学指导的新型CT图像的方法
Arjun Krishna1, Shanmukha Yenneti1, Ge Wang2
1Computer Science Department, Stony Brook University, Stony Brook, New York, USA.
Medical physics
|December 3, 2023
概括
这项研究引入了一种新的框架,用于为深度学习生成无限注释的计算机断层扫描 (CT) 图像. 该方法通过克服有限的训练数据挑战来提高医疗AI性能.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 医疗应用中的深度学习受到大型注释数据集稀缺的阻碍.
- 数据不足限制了深度学习网络捕捉图像细微差别的能力,影响了诊断准确性,并增加了对对手攻击的脆弱性.
研究的目的:
- 开发一个框架来合成大量的新型,高分辨率的计算机断层扫描 (CT) 图像和注释.
- 解决医疗深度学习应用中有限的标记数据的关键问题.
主要方法:
- 一个使用StyleGAN,U-Net和CycleGAN/Pixel-to-Pixel (P2P) 架构来增强一个小的注释肺CT数据集的框架.
- 利用一个小的注释数据集 (30名患者) 和一个大的非注释数据集 (14k名患者) 来创建一个广泛的注释数据集.
主要成果:
- 证明了肺部CT扫描与新注释的成功合成,优于仅生成图像的模型.
- 通过涉及医疗专业人员的视觉图灵测试来验证框架的有效性.
结论:
- 该框架可以在所有Hounsfield Unit (HU) 窗口中生成无限量的注释CT图像,具有高解剖可信度.
- 这种方法可以作为医学成像深度学习中注释数据增强的通用解决方案.
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