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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.
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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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msemalign: a pipeline for serial section multibeam scanning electron microscopy volume alignment.

Paul V Watkins¹, Eric Jelli¹, Kevin L Briggman¹

¹Max Planck Institute for Neurobiology of Behavior-caesar, Bonn, Germany.

Frontiers in Neuroscience

|December 27, 2023

Summary

Serial section multibeam scanning electron microscopy (ssmSEM) enables rapid 3D neural connectome acquisition. A new pipeline, msemalign, efficiently aligns massive datasets, minimizing distortions for accurate neural anatomy and synaptic mapping.

Keywords:

SEM alignment multibeam pipeline serial section electron microscopy

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Last Updated: Jul 7, 2025

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Area of Science:

Neuroscience
Electron Microscopy
Computational Biology

Background:

Serial section multibeam scanning electron microscopy (ssmSEM) is a leading technology for high-resolution 3D neural tissue imaging.
Acquiring petabyte-scale datasets (e.g., 1 mm^3) is crucial for detailed connectomics, revealing neuronal morphology, synapses, and organelle distribution.
The ssmSEM process generates millions of image tiles per dataset, necessitating robust alignment for 3D volume reconstruction.

Purpose of the Study:

To introduce msemalign, a novel alignment pipeline for ssmSEM datasets.
To address the challenges of aligning petabyte-scale 3D neural imaging data.
To achieve scalable and simple alignment with minimized section distortions.

Main Methods:

Development of the msemalign software pipeline.
Application of the pipeline to align large-scale ssmSEM datasets.
Evaluation of alignment quality focusing on smooth transitions and minimized distortions.

Main Results:

The msemalign pipeline successfully aligns petabyte-scale ssmSEM datasets.
Achieved smooth transitions across the 3D volume during navigation.
Significantly minimized the magnitude of section distortions compared to original micrographs.

Conclusions:

msemalign provides a scalable and effective solution for aligning large 3D neural connectome datasets.
The pipeline facilitates accurate reconstruction of neuronal structures and synaptic connectivity.
Enables more reliable analysis of complex neural circuits from high-resolution electron microscopy data.