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Scanning Electron Microscopy

Scanning Electron Microscopy

A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...

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Related Experiment Video

Updated: May 15, 2026

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Published on: July 15, 2021

Efficient scanning for EM based target localization.

Raphael Sznitman¹, Aurelien Lucchi, Natasa Pjescic-Emedji

¹Computer Vision Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland. firstname.lastname@epfl.ch

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|January 5, 2013

Summary

This summary is machine-generated.

This study introduces a fast electron microscopy (EM) imaging strategy. It accelerates cell morphology studies by rapidly identifying and scanning relevant regions, saving significant time.

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Area of Science:

Cell Biology
Microscopy Techniques

Background:

Electron microscopy (EM) offers nanometer resolution crucial for cell morphology studies.
Current EM imaging is time-consuming, limiting sample throughput and size.
Efficiently acquiring EM image series is a key challenge in biological research.

Purpose of the Study:

To develop a strategy for accelerating electron microscopy (EM) imaging.
To enable automated selection of regions of interest (ROIs) for faster imaging.
To improve the efficiency of imaging biological samples for morphological analysis.

Main Methods:

A novel iterative scanning approach for EM was developed.
The strategy involves initial low-resolution scanning to identify ROIs.
Subsequent high-resolution scans are focused on identified ROIs, with iterative refinement.

Main Results:

The new EM imaging strategy significantly reduces acquisition time.
Localization accuracy for mitochondria and synapses is comparable to state-of-the-art methods.
The approach achieves results in approximately one-tenth of the time of conventional methods.

Conclusions:

This fast EM imaging strategy dramatically enhances throughput for cell morphology studies.
Automated ROI selection coupled with iterative scanning offers a powerful alternative to current EM techniques.
The method provides a substantial time-saving solution for high-resolution biological imaging.