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Updated: Jun 17, 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

A framework for automatic segmentation in three dimensions of microstructural tomography data.

P S Jørgensen¹, K V Hansen, R Larsen

¹Fuel Cells and Solid State Chemistry Division, Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Building 778, Frederiksborgvej 399, 4000 Roskilde, Denmark. psjq@risoe.dtu.dk

Ultramicroscopy

|December 17, 2009

Summary

This summary is machine-generated.

This study introduces an automated framework for segmenting complex material microstructures, overcoming limitations of manual methods. The novel approach enhances the efficiency and precision of 3D microstructure analysis, particularly for focused ion beam serial sectioning data.

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Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

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Last Updated: Jun 17, 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

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

Area of Science:

Materials Science
Computational Science
Image Analysis

Background:

Quantitative 3D analysis of material microstructure using focused ion beam (FIB) serial sectioning is often hindered by time-consuming manual segmentation.
Existing automatic segmentation methods may lack the required accuracy or robustness for complex microstructures.

Purpose of the Study:

To develop an efficient and accurate framework for automatic segmentation of complex 3D microstructures.
To overcome the limitations of manual delineation and improve the quantitative analysis of material microstructure.

Main Methods:

A novel framework utilizing a level set method based on numerical approximations to partial differential equations.
3D surface evolution driven by vector fields derived from experimental data.
Incorporation of sub-voxel precision and surface curvature constraints for smooth segmentation.

Main Results:

Successful automatic segmentation of complex microstructures in 3D.
Achieved sub-voxel precision for segmented surfaces.
Demonstrated the framework's applicability using solid oxide cell materials.

Conclusions:

The proposed framework offers an efficient and precise solution for automatic 3D microstructure segmentation.
This advancement facilitates routine quantitative analysis of complex materials.
The method shows promise for applications in materials science, particularly with FIB serial sectioning data.