自主训练的深卷积神经网络用于CT中的降噪.
Zhongxing Zhou1, Akitoshi Inoue1, Cynthia H McCollough1
1Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States.
Journal of medical imaging (Bellingham, Wash.)
|August 28, 2023
概括
一个新型的自我训练深卷积神经网络 (ST_CNN) 有效地减少了临床CT图像中的噪音,而不需要外部数据集. 这种方法实现了与传统CNN相比的图像质量,增强了肝损伤可视化.
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:
- 监督深层卷积神经网络 (CNN) 用于临床CT降噪.
- 训练这些网络需要大型,配对的数据集,这些数据集很难获得,可能缺乏通用性.
研究的目的:
- 为CT图像减少噪音提出一个自训练的深度CNN (ST_CNN),消除了对预先存在的训练数据集的需求.
- 开发一种在不同成像条件下可通用的方法.
主要方法:
- 在投影领域进行了广泛的数据增强,包括噪声插入和旋转增强.
- 自我训练方法,网络从患者自己的数据中学习.
- 来自同一患者的配对低质量和高质量图像的重建,用于训练.
主要成果:
- ST_CNN显示了与传统的CNN相比可比的峰值信号与噪声比率和结构相似度指数.
- ST_CNN显示了噪声质感的改善,肝膜的均性,以及肝损伤的更好的主观可视化.
- 观察到血管清晰度的轻微牺牲,但没有影响诊断可见性.
结论:
- 自学深度CNN (ST_CNN) 方法可以实现与传统深度CNN反驳方法相似的图像质量.
- ST_CNN为CT降噪提供了可行的替代方案,特别是当外部培训数据集不可用或不适合时.
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