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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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Related Experiment Video

Updated: Jan 14, 2026

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

Published on: October 21, 2018

A benchmark dataset and baseline methods for rock microstructure interpretation in SEM images.

Yao Zhang^1,2, Xinming Wu³, Jiachun You⁴

¹State Key Laboratory of Precision Geodesy, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China.

Scientific Data

|October 22, 2025

Summary

Researchers created a new dataset of rock Scanning Electron Microscope (SEM) images. Deep learning models trained on this dataset significantly outperform traditional methods for rock microstructure segmentation.

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Last Updated: Jan 14, 2026

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Area of Science:

Geology
Materials Science
Image Analysis

Background:

Accurate segmentation of rock microstructures in Scanning Electron Microscope (SEM) images is crucial for geological analysis.
Existing deep learning (DL) segmentation methods are limited by a scarcity of labeled SEM datasets.

Purpose of the Study:

To develop a standardized, high-quality SEM dataset for rock microstructures.
To evaluate and compare the performance of DL models against traditional methods for SEM image segmentation.
To provide a benchmark dataset and code for advancing automated rock image analysis.

Main Methods:

Developed a standardized SEM dataset featuring mudstone, sandstone, and shale.
Applied preprocessing techniques including magnification standardization, median filtering, and adaptive histogram equalization.
Compared traditional segmentation algorithms with state-of-the-art deep learning models.

Main Results:

Deep learning models demonstrated superior performance in segmenting complex rock microstructures compared to traditional methods.
The developed dataset and benchmark implementations facilitate reproducible research.
The study highlights the potential of DL for automated analysis of geological SEM images.

Conclusions:

The creation of a standardized SEM dataset significantly aids the development of automated rock microstructure analysis.
Deep learning models offer a powerful approach for accurate segmentation of geological SEM images.
Public release of the dataset and code promotes further advancements in the field.