关于使用CF填充和Pix2pix进行数据增强的虚拟牙图像生成的研究
Soo-Yeon Jeong1, Eun-Jeong Bae2, Hyun Soo Jang3
1Division of Software Engineering, Pai Chai University, Daejeon, 35345, Republic of Korea.
Scientific reports
|November 5, 2024
概括
使用人工智能生成虚拟牙图像可以简化牙假肢工作流程. 结合真实和人工智能生成的图像来训练深度学习模型显著提高了性能,接近仅使用真实患者数据的结果.
科学领域:
- 人工智能在牙科中的应用
- 数字牙科数字牙科
- 医疗成像医学成像
背景情况:
- 传统的牙假体是劳动密集型和耗时的.
- 深度学习模型可以自动化牙科假体过程,但需要大量的训练数据.
- 由于患者隐私问题,研究中使用真实牙图像受到限制.
研究的目的:
- 为训练人工智能模型开发一种生成现实的虚拟牙图像的方法.
- 为了应对对真实患者牙科数据的有限访问的挑战.
主要方法:
- 使用图像对图像翻译 (pix2pix) 来生成各种虚拟牙图像.
- 使用上下文重建填充 (CR-Fill) 来完善虚拟图像并确保解剖学准确性.
- 对比了在真实图像,虚拟图像和两者的组合上训练的深度学习模型的性能.
主要成果:
- 拟议的方法成功生成了虚拟牙图像,视觉上与真实口腔内扫描相似.
- 仅在虚拟图像上训练的模型表现不佳.
- 使用真实和虚拟图像组合的训练模型实现了与仅在真实图像上训练的模型相比的性能.
结论:
- 人工智能驱动的虚拟牙科图像生成是克服研究数据限制的可行解决方案.
- 混合训练数据集 (真实和虚拟图像) 为开发牙科假体中强大的AI模型提供了一个有希望的方法.
- 这种方法可以加速AI在数字牙科的开发和采用.
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