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Automated Joint Space Detection Improves Bone Segmentation Accuracy

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Phases and Interfaces from Real Space Atomically Resolved Data: Physics-Based Deep Data Image Analysis.

Rama K Vasudevan¹, Maxim Ziatdinov¹, Stephen Jesse¹

¹Institute for Functional Imaging of Materials and Center for Nanophase Materials Sciences, Oak Ridge National Laboratory , Oak Ridge Tennessee 37831, United States.

|August 13, 2016

Summary

This summary is machine-generated.

This study introduces a new deep data analysis method to interpret atomically resolved microscopy data. The approach reconstructs unknown structure factors, accelerating crystallographic analysis and enabling local structure-property determinations.

Keywords:

Fourier transform Unmixing atomic scale imaging crystallography scanning transmission electron microscopy scanning tunneling microscopy

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Area of Science:

Materials Science
Physics
Data Science

Background:

Electron and scanning probe microscopies provide high-resolution structural and electronic data.
Interpreting this data requires robust frameworks to link atomic structures to macroscopic properties.
Current methods face challenges in extracting detailed information from complex datasets.

Purpose of the Study:

To develop a physics-based framework for knowledge generation from atomically resolved microscopy data.
To create a method for reconstructing unknown structure factors and their spatial localization.
To enable automatic local structure-property determinations in crystalline and quasi-ordered systems.

Main Methods:

Synergy of sliding window Fourier transform to capture local structure factors.
Blind linear unmixing of the resultant 4D data set for deep data analysis.
Application to both synthetic and experimental electron and scanning tunneling microscopy data.

Main Results:

Successful reconstruction of a priori unknown structure factors of individual components.
Demonstration of spatial localization of these structure factors.
Validation of the approach using synthetic and real microscopy data.

Conclusions:

The developed deep data analysis method significantly speeds up crystallographic analysis in atomically resolved data.
This approach facilitates automatic local structure-property determinations.
It paves the way for understanding complex systems with competing structural and electronic orders.