Workflow automation of SEM acquisitions and feature tracking

• Published in: Ultramicroscopy 2025-03, Vol.269, p.114093, Article 114093
• Main Authors: Clusiau, Sabrina, Piché, Nicolas, Brodusch, Nicolas, Strauss, Mike, Gauvin, Raynald
• Format: Article
• Language: English
• Subjects: Automation; Computational microscopy; Electron microscope; Image processing; Quantitative microscopy
• ISSN: 0304-3991; 1879-2723; 1879-2723
• DOI: 10.1016/j.ultramic.2024.114093

•Fully automated workflows on the scanning electron microscope for quantitative analysis.•Low to high magnification grid imaging, stitching, segmentation, and quantitative analysis.•Guided acquisitions with feature tracking to image only areas of interest.•Integrating image processing tasks with acquisitions for smart beam positioning.•Total acquisition time is considerably reduced when features are tracked. Acquiring multiple high magnification, high resolution images with scanning electron microscopes (SEMs) for quantitative analysis is a time consuming and repetitive task for microscopists. We propose a workflow to automate SEM image acquisition and demonstrate its use in the context of nanoparticle (NP) analysis. Acquiring multiple images of this type of specimen is necessary to obtain a complete and proper characterization of the NP population and obtain statistically representative results. Indeed, a single high magnification image only scans a small area of sample, containing only few NPs. The proposed workflow is successfully applied to obtain size distributions from image montages at three different magnifications (20,000x, 60,000x and 200,000x) on the same area of the sample using a Python based script. The automated workflow consists of sequential repositioning of the electron beam, stitching of adjacent images, feature segmentation, and NP size computation. Results show that NPs are best characterized at higher magnifications, since lower magnifications are limited by their pixel size. Increased accuracy of feature characterization at high magnification highlights the importance of automation: many high-magnification acquisitions are required to cover a similar area of the sample at low magnification. Therefore, we also present feature tracking with smart beam positioning as an alternative to blind acquisition of very large image arrays. Feature tracking is achieved by integrating microscope tasks with image processing tasks, and only areas of interest will be imaged at high resolution, reducing total acquisition duration.