通过深度设计优化增强光板光显微镜照明束
Chen Li1,2, Mani Ratnam Rai1,2, Yuheng Cai1,2
1Joint Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695, USA.
bioRxiv : the preprint server for biology
|December 11, 2023
概括
研究人员利用深度学习优化了光板光显微镜 (LSFM) 照明. 这种方法提高了3D成像质量和大组织体积中的细胞检测.
科学领域:
- 光学和光子学 在光学和光子学.
- 生物医学成像技术 生物医学成像技术
- 计算科学 计算科学
背景情况:
- 光板光显微镜 (LSFM) 能够对清除的组织进行高分辨率的3D成像.
- LSFM 图像质量严重取决于光束特性,以实现最佳的光学分割.
- 传统的方法讨论了具有不同目标的最佳照明配置 (高斯式,贝塞尔式,艾里式).
研究的目的:
- 开发一种计算方法来优化LSFM照明束.
- 通过定制照明来提高细胞检测的深度学习模型性能.
- 为了提高大量LSFM数据集的图像质量和对比度.
主要方法:
- 在深度学习培训中集成了一个具有可变相罩的物理LSFM照明模型.
- 开发了一个用集成照明模型训练的细胞检测网络.
- 利用关节优化持续更新相位面罩以改善成像.
主要成果:
- 与传统的高斯光片相比,在成像质量和细胞检测方面取得了显著的改进.
- 通过模拟和实验LSFM数据验证了该方法.
- 为大型成像数据集展示了增强的图像质量和对比度.
结论:
- 深度学习引导的照明优化为LSFM提供了一种新的策略.
- 这种方法显示了在显微镜中推进光学设计的巨大潜力.
- 增强的成像质量可以更准确地分析大型生物数据集.
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