Across two recent webinars, we explored both the principles and the practical implementation of AI-based image analysis in electron microscopy.

The first session focused on the limitations of classical methods—thresholding, filtering, and rule-based segmentation—and how machine learning and deep learning overcome these challenges. The second session showed how these capabilities can be integrated into complete workflows, from image acquisition to automated data output.

Together, they highlight a clear shift in the field.

Technical Summary

Traditional image analysis methods remain effective for simple, high-contrast images, but they quickly break down when dealing with real-world SEM data—heterogeneous materials, varying contrast, noise, and complex morphologies.

Machine learning improves robustness by using statistical classification, but still requires user input and struggles with highly complex structures.

Deep learning, based on convolutional neural networks, represents a step change. By learning directly from examples, these models can recognize patterns in images in a way that approaches human visual interpretation, enabling segmentation of features that were previously difficult or impossible to isolate.

At the same time, practical implementation has matured significantly. Modern workflows now combine:

• Pre-trained models for rapid starting points
• Fast fine-tuning using small, application-specific datasets
• Standardized workflows (protocols) for repeatability
• Batch processing and automation for scalability

This allows users to move from single-image analysis to consistent, high-throughput data extraction.

Equally important is the ability to validate results. Metrics such as intersection-over-union provide a quantitative way to assess model performance and ensure that AI-generated results are reliable and defensible.

Conclusion

Taken together, these developments show that AI-based image analysis is no longer a niche or experimental capability in microscopy.

The key transition is not just improved segmentation accuracy, but the emergence of end-to-end workflows that connect image acquisition, AI-driven segmentation, quantitative measurement, and automated reporting.

For SEM users, this means that image analysis is evolving from a manual, time-consuming step into a scalable and reproducible process for generating data.

The question is no longer whether AI can segment complex microstructures—but how to integrate it effectively into everyday workflows to deliver consistent, quantitative results. 

Webinar 1 : Principles of SEM image analysis using AI/ML

Webinar 2 : Practical image analysis using AI for SEM