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用光复合和传感 终身成像显微镜 支持 Phasor U-Net
Yuanhua Liu1,2, Guiwen Luo3, Fang Zhao3
1School of Biomedical Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China.
Analytical chemistry
|May 16, 2025
概括
光器U-Net是一种深度学习工具,增强了光终身成像显微镜 (FLIM) 的准确性和速度. 这种方法改善了寿命估计,并使先进的多重成像能够在没有大量实验数据的情况下进行复杂成像.
科学领域:
- 生物物理学的生物物理.
- 材料科学 材料科学 材料科学
- 显微镜的使用方法
背景情况:
- 光终身成像显微镜 (FLIM) 对材料和生命科学至关重要,但由于光子数量有限和处理速度缓慢,它存在准确性问题.
- 目前的FLIM方法在精确的寿命估计和处理大数据集方面面临挑战,阻碍了先进的应用.
研究的目的:
- 推出Phasor U-Net,这是一个新的深度学习方法,用于快速准确的FLIM数据处理.
- 为了提高寿命估计的准确性,并减少FLIM中的数据处理时间.
- 证明该方法在多重成像和材料表征中的实用性.
主要方法:
- 开发了Phasor U-Net,这是一个深度学习模型,包含两个U-Net子网络用于denoising和deconvolution.
- 仅在计算机生成的数据集上训练模型,消除了对广泛实验数据的需求.
- 应用该方法来分析FLIM数据,包括生物样本的多重成像和量子点特性.
主要成果:
- 与直接相位分析相比,Phasor U-Net在相位图上实现了修改后的Kullback-Leibler分歧减少1.5-8倍.
- 该方法将终身图像的平均绝对误差降低了1.18-4.41倍.
- 成功展示了小鼠小肠样本的多重成像,并使用终身信息改进了量子点大小估计.
结论:
- 索U-Net在FLIM中提供了显著的进步,提供了更快,更准确的寿命估计.
- 该方法在合成数据上进行训练的能力使其具有广泛的适用性,并减少了实验负担.
- 这种深度学习方法为在各种科学领域使用FLIM的新基础研究铺平了道路.
相关概念视频
01:37Super-resolution Fluorescence Microscopy
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
01:25Fluorescence and Phosphorescence: Instrumentation
Fluorometers and spectrofluorometers are two types of instruments used for measuring molecular fluorescence. These instruments differ in how they select excitation and emission wavelengths and the type of light sources they utilize. Fluorometers use absorption interference filters to choose excitation and emission wavelengths. The excitation source in a fluorometer is typically a low-pressure mercury vapor lamp that emits intense lines distributed throughout the ultraviolet and visible regions.