在基于纤维束的光显微镜中提高分辨率和对比度,使用生成对抗网络
Amir Mohammad Ketabchi1, Berna Morova2, Yiğit Uysalli3,4
1Department of Electrical and Electronics Engineering, Koç University, Istanbul, Türkiye.
Journal of microscopy
|April 2, 2024
概括
生成对抗网络 (GAN) 增强光成像分辨率和对比度在纤维束 (FB) 内镜中. 这种深度学习方法使用多焦结构化照明显微镜 (MSIM) 数据进行训练,可以显著提高图像质量,而无需额外的硬件.
科学领域:
- 生物医学光学 生物医学光学
- 显微镜的使用方法
- 人工智能的人工智能
背景情况:
- 纤维束内镜 (FB) 对于微创生物和医学成像至关重要.
- 有限的数值孔径 (NA) 和纤维核心大小限制了FB光显微镜中的分辨率和对比度.
研究的目的:
- 为了提高通过纤维束内镜获得的光图像的分辨率和对比度.
- 为了提高图像质量,利用生成对抗网络 (GANs).
主要方法:
- 在公司内部制造高NA纤维束.
- 使用数字微镜装置 (DMD) 开发基于纤维束的多焦结构照明显微镜 (MSIM).
- 使用MSIM数据训练GAN模型进行图像到图像翻译.
主要成果:
- 该GAN模型有效地将广场图像转换为高分辨率的MSIM图像.
- 与原始广场图像相比,GAN生成的图像在对比度和分辨率上均显著改善.
- 在接受GAN图像增强培训后,不需要额外的光学硬件.
结论:
- 用MSIM数据训练的基于GAN的模型显示出强大的潜力,可以增强纤维束内镜中的广场光成像.
- 这种方法为克服纤维束显微镜固有的局限性提供了一条途径.
- 这项研究展示了深度学习在改善生物医学成像分辨率和对比度方面的实际应用.
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