Particle analysis is a common task in electron microscopy, whether for research or industrial applications. Accurately detecting and measuring particles can provide critical insights into material properties, quality control or process optimization.
However, segmenting particles in images is often a time-consuming and delicate process. Traditional methods may require careful parameter tuning, manual corrections or complex workflows, especially when dealing with agglomerates or noisy images.
Recent advances in artificial intelligence are changing this landscape. Benoit Zupancic, product manager for SEM applications at Digital Surf explores how AI segmentation can simplify particle analysis and how it can be applied effectively in real-world microscopy images.
Why use AI for particle segmentation?
AI-based segmentation brings significant advantages compared to traditional image processing approaches. First, it dramatically reduces the time required to obtain reliable results. Instead of manually adjusting thresholds or filters, users can apply a trained model to detect particles automatically. Second, AI improves consistency. Because segmentation relies on trained models rather than manual parameter adjustments, it delivers more consistent and reproducible results across different images and users. Finally, AI can better handle complex situations such as agglomerated particles or variations in contrast.
Above. AI-powered segmentation enables faster, more reliable particle detection.
From a user perspective, one of the key benefits is simplicity. Modern AI tools are designed to be accessible, allowing users to perform advanced segmentation with minimal training.
How to perform AI-based particle segmentation
Using AI segmentation in your workflow can be straightforward. A typical process involves only a few steps:
- Import your microscopy image (obtained with electron microscopy or other types of microscopy or profilometry)
- Apply an AI segmentation tool to automatically detect particles
- Review the segmentation result and adjust if necessary
- Extract particle measurements such as size, distribution or morphology
In many cases, the segmentation can be performed in just a few clicks, making it accessible even to non-expert users. For advanced users, additional options may be available to refine the segmentation or adapt it to specific datasets.
Above. Calculate particle metrics such as size, distribution and morphology and generate statistics.
When does AI segmentation work best?
AI segmentation is particularly effective in a number of common scenarios. It performs very well when analyzing homogeneous particle populations, where particles share similar sizes and shapes. It is also well suited for moderately agglomerated particles, where boundaries remain partially distinguishable. These situations are frequently encountered in materials science and industrial quality control.
Above. AI-driven segmentation delivers accurate detection of both round and irregular particles, even in images with uneven lighting, contrast variations or overlapping features.
Limitations and practical considerations
While AI segmentation is highly efficient, it is important to understand its limitations. Performance may decrease when dealing with highly polydisperse samples, where particle sizes vary significantly. Similarly, very elongated or irregular (oblong) particles can be more difficult for the model to segment accurately. In such cases, combining AI segmentation with manual correction or complementary image processing tools can help achieve optimal results. Being aware of these limitations allows users to apply AI segmentation more effectively.
Key benefits for your workflow
Integrating AI segmentation into your particle analysis workflow offers several tangible benefits:
- Ease of use: perform complex segmentation with minimal setup
- Time saving: reduce analysis time from minutes to seconds
- Reproducibility: obtain consistent results across datasets and users
- Efficiency: focus on interpreting results rather than preparing data
These advantages make AI segmentation an attractive solution for both routine analysis and more advanced applications.
Conclusion
AI-powered segmentation is transforming the way particles are detected and analyzed in electron microscopy. By combining speed, robustness and ease of use, it enables more efficient workflows while maintaining high-quality results. Although it may not replace traditional methods in every situation, it provides a powerful and accessible tool for a wide range of applications.
Try it out yourself
Want to see how AI segmentation can improve your particle analysis workflow? Download a free trial of our software or request a live demo to test it on your own microscopy images.
Author : Benoit Zupancic