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Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

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FFT pattern recognition of crystal HRTEM image with deep learning.

Quan Zhang¹, Ru Bai², Bo Peng³

¹School of Computer Science, Southwest Petroleum University, Chengdu 610500, China; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Micron (Oxford, England : 1993)

|January 11, 2023

Summary

This study introduces an automated method using deep learning and computer vision to rapidly analyze high-resolution transmission electron microscope (HRTEM) images. The technique effectively identifies material phases in complex samples, significantly reducing manual analysis time for researchers.

Keywords:

Attention mechanism Computer vision Deep Learning High-resolution transmission electron microscope Local contrast Phase identification

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Area of Science:

Materials Science
Data Science
Microscopy

Background:

Manual analysis of transmission electron microscope (TEM) data is time-consuming.
In-situ TEM experiments generate large datasets requiring efficient processing.

Purpose of the Study:

To develop a rapid, automatic method for processing high-resolution transmission electron microscope (HRTEM) images.
To assist researchers by automating the analysis of crystal structures and material phases.

Main Methods:

Combines deep learning (LCA-Unet) and computer vision for automated HRTEM image analysis.
Utilizes 2D Fast Fourier Transform (FFT) on image patches to extract diffraction spots.
Employs a three-step process: spot coordinate calculation, material phase identification via PDF matching, and phase region merging.

Main Results:

The LCA-Unet effectively extracts weak bright spots from FFT images.
Automatic FFT pattern recognition accurately determines lattice spacings and inter-plane angles.
Phase identification and merging show consistency with manual analysis for zirconium and its oxide nanoparticles.

Conclusions:

The proposed method offers a significant advancement in automated HRTEM image analysis.
It enables rapid and effective identification of phase regions, accelerating materials research.
Processing a 4K x 4K HRTEM image takes approximately 3 seconds on a modern GPU-equipped computer.