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Machine Learning &
Microscopy Solutions

Software development for efficient analysis
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Materials science issues
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Incorporation of artificial intelligence
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GxP-compliant software solutions
Maximum efficiency in quantitative microscopy is one of our core areas of focus. Together with our partners, we are driving this forward through specific applications. Customised solutions can significantly increase the level of automation in this area. To this end, we offer sample holder systems for sectioned samples or customised samples. Automated batch testing whilst maintaining maximum flexibility presents a particular challenge. Machine learning? We apply it in a targeted manner!

Sample holder for efficient image capture

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Sample holders for scanning electron microscopy and
for highly efficient imaging in light microscopy

Our sample holders enable fast and reliable sample loading for efficient and reproducible imaging. They are compatible with all ZEISS optical and electron microscopes.

Our sample holders feature a flexible, modular design and can be easily adapted to different sample geometries. Microscopes from other manufacturers can also be integrated upon request.

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GxP-compliant software solutions as a business partner of ZEISS Microscopy

Support for GxP-compliant validation solutions for microscopy.

We implement automated analysis processes in accordance with, among other standards, the guidelines of the U.S. Food and Drug Administration (FDA, 21 CFR Part 11), ensuring that all electronic data complies with GxP requirements. Structured user management with clearly defined access rights, along with comprehensive documentation of the analysis workflow in an audit trail, ensures full traceability of all actions performed. Manipulation of the results data is impossible; all processes are documented in an audit-proof manner and are subject to rigorous version and change control procedures.

The analysis workflows are developed according to the V-Model, which optimally supports the requirements of the regulated environment and ensures the highest level of security and transparency.

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Materials Science Issues

Machine learning-based particle size analysis for various materials.

We offer fully automated, AI-powered quality assurance and material characterization, ranging from residual analysis with VDA 19-compliant reporting to advanced quantitative microstructural analyses. Integrated calibration ensures measurement accuracy, while large-area, high-throughput analyses efficiently capture process effects, microstructural homogeneity, and pore distributions across multiple samples. Using a “tiling” approach, we offer field- and region-specific analyses, including the visualization of larger defects, fine geometric evaluations, and grain size determination.

AI-powered workflows encompass semi-automated measurements of geometric parameters and the automated quantification of microstructural features such as grains, phases, or defects, which can be integrated into SQL databases to enable reproducible data management, supported by the automated generation of custom reports to assist with audits. By combining AI, automation, high-throughput analysis, and data-driven analytics, our solutions deliver highly accurate, reproducible insights that accelerate quality assurance, process optimization, and materials research for a variety of materials.

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Machine Learning und Data-driven Solutions in der Anwendung

Automatisierungslösungen in der Mikroskopie mit KI-Unterstützung. Fotograf/Bildquelle Sven Doering

Unser Labor kombiniert automatisierte Mikroskopie mit KI-gestützter Materialanalyse und liefert schnelle, genaue und quantitative Erkenntnisse aus Mikrostrukturen. Wir trainieren leistungsstarke Maschinen- und Deep-Learning-Modelle für die Fehlererkennung, Strukturklassifizierung, Eigenschaftskorrelationen und Mikrostrukturquantifizierung unter Verwendung von Frameworks wie TensorFlow, Keras, PyTorch, OpenCV und scikit-learn. Machbarkeitsstudien werden sowohl an bestehenden Datensätzen als auch an neu erworbenen Proben durchgeführt.

Unsere KI-Pipelines legen Wert auf Reproduzierbarkeit und Compliance durch benutzerdefinierte Modellexporte und standardisierte KI-Systemklassifizierung und -validierung. Wir sorgen für Transparenz durch detaillierte Modell- und Datenkarten, die Architektur und Limitationen dokumentieren, während die integrierte Datenabweichungsüberwachung die Genauigkeit über einen längeren Zeitraum hinweg gewährleistet, um Zuverlässigkeit, Robustheit und Audit-Bereitschaft sicherzustellen.

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