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Electron Microscope Tomography and Single-particle Reconstruction

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

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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相关实验视频

Updated: Mar 15, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

语境高通量3D体积电子显微镜数据采集使用人工智能

Tereza Hurník Konečná¹, Radek Jančík¹, Daniela Slamková¹

¹Materials and Structural Analysis Division, Thermo Fisher Scientific, Vlastimila Pecha 1282/12, Brno 62700, Czech Republic.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|March 13, 2026

概括

适应扫描使用人工智能来创建面具,使得3D体积电子显微镜 (vEM) 的高分辨率成像成为可能. 这种动态解析方法显著减少了各种样本中大型数据集的采集时间.

关键词:

3D电子显微镜 3D电子显微镜在FIB-SEM中.人工智能的人工智能是人工智能.深度学习是一种深度学习.音量音量音量音量

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Last Updated: Mar 15, 2026

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科学领域:

电子显微镜电子显微镜
人工智能的人工智能
生物成像是一种生物成像.

背景情况:

产生大型3D体积电子显微镜 (vEM) 数据集是耗时的.
广泛的生物结构的高分辨率成像存在重大挑战.

研究的目的:

开发一种人工智能驱动的方法来加速3D vEM数据采集.
引入动态分辨率扫描策略,以实现高效的VEM成像.

主要方法:

实现人工智能 (AI) 算法以生成兴趣区域面罩.
开发了一种适应扫描技术,用于动态分辨率数据捕获.
在各种生物样本中应用该方法,包括大脑,寄生虫,细胞和植物.

主要成果:

适应扫描显著减少了数据采集时间.
该方法实现了目标结构的高分辨率成像,同时以较低分辨率捕捉周围区域.
证明了样品不可知论,证明了与各种生物和组织的兼容性.

结论:

多分辨率扫描策略可以提高3D vEM.
这种方法可以节省多达两倍或更多的时间.
允许更高效,更具成本效益地生成更大的vEM数据集,以增加统计能力.