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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...

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Updated: May 23, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

通过深度学习用于3D电子显微镜的自动细胞结构提取.

Jin Kousaka¹, Atsuko H Iwane^2,3,4, Yuichi Togashi^5,6,7

¹Graduate School of Life Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, 525-8577, Kusatsu, Shiga, Japan.

Scientific reports

|May 20, 2025

概括

此摘要是机器生成的。

本研究介绍了一种自动化的深度学习系统,用于从电子显微镜图像中对3D细胞结构进行细分和重建. 该方法显著减少了手工劳动,从而实现了高效的3D细胞建模.

关键词:

生物图像分析分析细胞分裂细胞分裂在FIB-SEM中.有机器人有机器人分段化分段化分段化分段化

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

Published on: August 13, 2014

相关实验视频

Last Updated: May 23, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

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Published on: September 28, 2019

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

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科学领域:

细胞生物学细胞生物学
生物技术是生物技术.
显微镜的使用方法

背景情况:

3D细胞和组织建模在生物学中至关重要.
电子显微镜图像的手动细分是耗时的.
需要自动化解决方案来分析复杂的细胞结构.

研究的目的:

开发基于深度学习的自动化系统,用于对生物图像进行细分.
为了实现细胞和器官的精确3D重建.
为了克服手动图像分析的瓶.

主要方法:

使用聚焦离子束扫描电子显微镜 (FIB-SEM) 来获取图像.
在单细胞图像中使用U-Net模型进行器官细分.
集成了分段任何模型 (SAM) 和3D分水算法用于细胞提取和3D模型创建.

主要成果:

从连续电子显微镜图像中成功自动创建3D细胞模型.
使用训练有素的U-Net.证明了细胞有机体的准确细分.
能够有效地从大型数据集中提取单个3D细胞图像.

结论:

开发的系统完全自动化了3D细胞模型的创建.
深度学习和图像处理的进步可以提高细分精度.
这种方法加速了细胞结构的分析.