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Deep learning approaches for dislocation segmentation in TEM.

Assya Boughrara1, Christine Viala1, Laurent Dupuy2

  • 1Université de Toulouse, CNRS, CEMES, Toulouse, France.

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|February 26, 2026
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Summary
This summary is machine-generated.

Deep learning models, including semi-supervised learning, significantly improve dislocation segmentation in transmission electron microscopy (TEM) images, approaching expert performance. This facilitates faster analysis of material properties.

Keywords:
Deep learningDislocation segmentationSupervised and semi-supervised learning

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Data Science

Background:

  • Dislocation dynamics critically influence the mechanical properties of alloys.
  • Analyzing dislocations using transmission electron microscopy (TEM) requires specialized knowledge and is time-intensive.

Purpose of the Study:

  • To develop deep learning approaches for automated dislocation segmentation in TEM images.
  • To enhance the efficiency and accuracy of dislocation analysis across diverse materials and imaging conditions.

Main Methods:

  • Implementation of fully supervised learning (FSL) and semi-supervised learning (SSL) using an encoder-decoder neural network with a boundary-type loss.
  • Utilizing a large in-house unlabeled dataset to enrich feature descriptions in SSL.
  • Exploring domain adaptation with physically-grounded synthetic images generated via simulation.

Main Results:

  • SSL approaches demonstrated improved evaluation metrics compared to FSL, nearing human expert performance.
  • Direct transfer of knowledge from synthetic to real images showed limited success.
  • Synthetic images proved beneficial for improving predictions in challenging imaging scenarios.

Conclusions:

  • Deep learning, particularly SSL, offers a powerful tool to automate and improve dislocation segmentation in TEM.
  • While synthetic data has limitations for direct knowledge transfer, it can aid in specific challenging imaging conditions.
  • Automated dislocation density measurement is a promising future application.