{"id":8317,"date":"2026-09-11T09:32:08","date_gmt":"2026-09-11T07:32:08","guid":{"rendered":"https:\/\/www.scs.ch\/uncategorized-en-us\/ai-at-the-edge-counting-cells\/"},"modified":"2026-09-20T07:38:57","modified_gmt":"2026-09-20T05:38:57","slug":"ai-at-the-edge-counting-cells","status":"publish","type":"post","link":"https:\/\/www.scs.ch\/en\/case-studies\/ai-at-the-edge-counting-cells\/","title":{"rendered":"AI at the Edge \u2013 Counting Cells"},"content":{"rendered":"

Initial Situation<\/h2><\/div>

Cell growth is monitored in biology, biotechnology, and pharmaceuticals for numerous applications, such as testing different substances. However, manual cell counting in microscopy images is time-consuming. Automated image analysis can take over this step. The challenge: typical lab PCs offer limited computing power, yet fast and reliable results are needed.<\/p>\n<\/div>

\"Orangefarbene<\/span><\/div><\/div><\/div>

Project Overview<\/h3><\/div>

Client<\/strong>
Manufacturer of zenCELL owl<\/p>\n<\/div>

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Sector<\/strong>
Medical & Life Science<\/p>\n<\/div>

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Solution<\/strong>
Deep-learning cell counting directly on the lab PC.<\/p>\n<\/div>

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System Environment <\/strong>
\nzenCELL owl, compact and stackable microscopy system for incubators, PC-based analysis<\/p>\n<\/div>

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Special Feature <\/strong>
\noptimized for local operation on resource-constrained lab PCs<\/p>\n<\/div>

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Application <\/strong>
\nsimultaneous monitoring of 24 samples<\/p>\n<\/div><\/div><\/div><\/div><\/div>

SCS’s Contribution<\/h2><\/div>

SCS developed a deep-learning algorithm for automatic cell counting and specifically optimized the neural network for fast analyses on limited computing power.<\/p>\n<\/div><\/div><\/div>

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Cells Counted Automatically<\/h4><\/div>
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The algorithm developed by SCS recognizes and segments cells in microscopy images, automatically determining their count.<\/p>\n<\/div><\/div><\/div>

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Reference Model Built First<\/h4><\/div>
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SCS initially trained a model on high-performance hardware that reliably segments cells. This reference model formed the basis for subsequent optimization for limited computing power.<\/p>\n<\/div><\/div><\/div>

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Streamlined Step by Step<\/h4><\/div>
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Starting from the reference model, SCS specifically simplified the neural network and, after each step, checked how the changes affected accuracy and speed.<\/p>\n<\/div><\/div><\/div>

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Comparable Quality with Less Computing Power<\/h4><\/div>
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The optimized neural network delivers fast results with comparable quality to the more powerful reference model, even on a resource-constrained lab PC.<\/p>\n<\/div><\/div><\/div>

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\"Box-and-Whisker-Diagramm,<\/span><\/div><\/div><\/div><\/div><\/div>
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Result<\/h2><\/div>

With the algorithm developed by SCS, cell counts can be determined automatically, even on existing lab hardware. The zenCELL owl can simultaneously monitor up to 24 samples in the incubator and display their development on a PC, with the software automatically determining both cell count and confluence. This eliminates a time-consuming manual step for scientists. According to the project assessment, automatic analysis saves many hours with comparable quality, without the need to purchase more powerful hardware.<\/p>\n<\/div><\/div><\/div>

24 samples<\/h3><\/div>

monitored simultaneously in the incubator.<\/p>\n<\/div><\/div><\/div>

many hours<\/h3><\/div>

less manual effort with comparable quality.<\/p>\n<\/div><\/div><\/div>

no new hardware<\/h3><\/div>

optimized for existing lab computing power.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

Services Used<\/h2><\/div><\/div><\/div>

Algorithms, ML & AI<\/h3><\/div><\/div><\/div><\/div><\/div><\/div>

Computer Vision<\/h3><\/div><\/div><\/div><\/div><\/div><\/div>

Edge Computing<\/h3><\/div><\/div><\/div><\/div><\/div><\/div>

Medical & Life Science<\/h3><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>
\"Smiling<\/div><\/div>

Your Contact Person<\/h2><\/div>
Beat H\u00f6rmann<\/div><\/div>

Healthtech & Innovation
\n+41 43 456 16 00<\/a><\/p>\n<\/div>

Speak with the responsible specialist<\/span>Speak with the responsible specialist<\/span><\/div><\/a><\/div>

For questions about Machine Learning, Computer Vision, and AI models for resource-constrained hardware.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

More Case Studies<\/h2><\/div>