在低剂量CT无声化任务之间转移U-Net:一项具有不同空间分辨率的验证研究.
Xin Zhang1,2, Ting Su1, Yunxin Zhang3
1Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Quantitative imaging in medicine and surgery
|January 15, 2024
概括
低剂量计算机断层扫描 (LDCT) 的深度学习模型可以在不同的空间分辨率上传. 然而,通过一个小的数据集重新训练U-Net模型,可以显著减少文物并提高图像质量.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 深度学习,特别是U-Net,越来越多地用于低剂量计算机断层扫描 (LDCT),以在减少辐射剂量时提高图像质量.
- 验证训练有素的深度学习模型在不同成像系统的各种LDCT数据集中的通用性,对于临床采用至关重要.
研究的目的:
- 评估预先训练的U-Net在不同空间分辨率的LDCT图像上的拒绝性能的可复制性.
- 调查空间分辨率差异对U-Net拒绝可转移性的影响,并确定潜在的文物.
主要方法:
- 在LDCT图像上训练了一个U-Net模型,然后在六个不同的空间分辨率 (62.5625μm) 的数据集中验证.
- 量化指标包括剩余方差,PSNR,NRMSE和SSIM用于绩效评估.
- 用数据子集重新训练网络被探索以减轻交叉分辨率文物.
主要成果:
- 虽然U-Net在不同空间分辨率的LDCT图像中证明了其有效性,但交叉验证引入了图像工件.
- 文物变得更加明显,空间分辨率的不一致性越来越大,特别是在对象边缘和中心.
- 使用大约20%的原始数据重新训练U-Net,显著减少了文物 (NRMSE从0.1898下降到0.1263,SSIM从0.7558增加到0.8036).
结论:
- 将为LDCT训练的U-Net转移到具有不同空间分辨率的数据集可能会导致文物生成.
- 建议将U-Net与所需空间分辨率的小,有针对性的数据集进行重新训练,以保持最佳的无声化性能并最大限度地减少文物.
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