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Updated: Jun 6, 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

Unsupervised Segmentation and Clustering Workflow for Efficient Processing of 4D-STEM and 5D-STEM Data.

Serin Lee¹, Stephanie M Ribet², Arthur R C McCray¹

¹Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305USA.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|June 4, 2026

Summary

This summary is machine-generated.

A new clustering framework segments four-dimensional scanning transmission electron microscopy (4D-STEM) data, identifying distinct crystallographic domains. This method enhances data analysis for accurate structure and strain mapping, especially in dynamic experiments.

Keywords:

4D-STEM 5D-STEM clustering orientation strain

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Last Updated: Jun 6, 2026

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Published on: August 13, 2014

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Published on: February 28, 2021

Area of Science:

Materials Science and Engineering
Electron Microscopy
Crystallography

Background:

Four-dimensional scanning transmission electron microscopy (4D-STEM) provides high-resolution structural and strain information.
Increasing data complexity in 4D-STEM and 5D-STEM necessitates advanced segmentation techniques for in situ experiments.
Current analysis methods struggle with large datasets and identifying structurally consistent behavior across sequential measurements.

Purpose of the Study:

To develop a robust clustering framework for segmenting crystallographically distinct domains within 4D-STEM datasets.
To improve the efficiency and physical meaningfulness of analyzing complex, high-dimensional STEM data.
To enable rapid and accurate mapping of orientation, phase, and strain from large datasets.

Main Methods:

A novel clustering framework utilizing local diffraction-pattern similarity as a metric.
Extraction of spatially contiguous regions using closed contours to delineate crystallographically distinct domains.
Generation of cluster-averaged diffraction patterns for improved signal quality and data compression.

Main Results:

Successful identification of crystallographically distinct domains in 4D-STEM datasets.
Significant data volume reduction (orders of magnitude) with improved signal quality.
Demonstrated applicability to in situ liquid-cell 4D-STEM data of gold nanoparticle growth, enabling accurate structure-strain mapping.

Conclusions:

The developed clustering framework offers a scalable and generalizable solution for segmenting 4D-STEM data.
The method facilitates efficient data compression and quantitative structure-strain mapping across various 4D-STEM modalities.
Publicly available code and workflows promote reproducibility and reuse in advanced electron microscopy analysis.