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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection.

Sandeep Madireddy¹, Ding-Wen Chung², Troy Loeffler³

¹Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, United States.

Scientific Reports

|December 29, 2019

Summary

This summary is machine-generated.

We developed a deep learning method for automatically identifying interfaces in atom-probe tomography (APT) data. This approach accurately segments microstructural phases without manual labeling, improving analysis of interfacial properties.

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Area of Science:

Materials Science
Nanotechnology
Data Science

Background:

Atom-probe tomography (APT) is crucial for atomic-scale material characterization, particularly for interfacial analysis in multiphase systems.
Traditional interface identification in APT data relies on subjective, manual methods prone to inconsistencies and scalability issues.
These conventional techniques struggle with local composition variations, limiting accurate interfacial property extraction.

Purpose of the Study:

To introduce a novel, automated digital image segmentation approach for analyzing APT data.
To enable accurate and efficient segmentation of different phases and extraction of interfacial properties from APT datasets.
To overcome the limitations of manual and subjective methods in identifying interfaces within complex material structures.

Main Methods:

A deep neural network-based digital image segmentation technique was employed.
The model leverages knowledge transfer from natural image datasets for phase segmentation.
The approach was validated on a system with a precipitate phase in a matrix, exhibiting layered, isolated, and interconnected interface types.

Main Results:

The deep learning method successfully segmented APT data into distinct phases, automatically identifying interfaces.
The segmentation approach demonstrated efficiency and accuracy without requiring labeled interface data for training.
Qualitative visualization and quantitative comparisons confirmed the reliability of the automated segmentation for interfacial property analysis.

Conclusions:

Deep neural network-based segmentation offers an efficient and objective alternative for analyzing APT data.
This automated method significantly improves the characterization of interfacial properties in granular and heterophase materials.
The approach eliminates the need for manual intervention and expensive labeling, making APT data analysis more scalable and consistent.