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Three-Dimensional Microscopy in Microbiology

Three-Dimensional Microscopy in Microbiology

Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...

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Updated: Jun 20, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

卷度定位显微镜与深度学习.

Keyi Han^1,2, Xuanwen Hua^1,2, Tianrui Qi^1,3

¹Laboratory for Systems Biophotonics, Georgia Institute of Technology, Atlanta, GA, USA.

Nature communications

|December 8, 2025

概括

此摘要是机器生成的。

具有深度学习的体积定位显微镜 (VLM) 可实现高准确度的3D单分子成像. 这种超分辨率技术为先进的生物医学研究提供了硬件简单性和数据效率.

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Last Updated: Jun 20, 2026

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High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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科学领域:

生物物理学的生物物理.
光学显微镜的使用方法
计算生物学计算生物学

背景情况:

超分辨率显微镜,特别是基于本地化的方法,在平衡光学复杂性,计算负载和用户可访问性方面面临挑战.
目前的方法经常单独使用确定性或基于学习的方法,缺少协同潜力.

研究的目的:

引入体积定位显微镜 (VLM) 与深度学习,用于高准确度的3D单分子成像.
整合仪器和算法创新,以改善超分辨率显微镜.

主要方法:

在单分子数据采集中,VLM使用了波面优化的光场配置.
一个级联的神经网络重建3D体积并确定分子坐标.
该系统使用系统意识的内在点差函数进行训练,消除了对外部模式或样本特定数据的需求.

主要成果:

实现了10nm横向和25nm轴向定位精度.
已证明有效的成像深度超过4微米.
在各种生物标本中得到验证,显示硬件简单性,数据效率和最小的光毒性.

结论:

具有深度学习的VLM提供了用于3D单分子成像的强大,可访问的超分辨率技术.
这种方法克服了当前光显微镜的局限性,为更广泛的生物医学研究进步铺平了道路.