Adherent cells
Track confluency, growth, and repeatable cell counts over time from label-free images.
Segmented
Raw
“ How confluent is this flask, right now? ”
“ How many cells are here, and how many are still viable? ”
“ Did the scratch close faster under treatment? ”
“ How have my spheroids grown across every timepoint? ”
Used by researchers at
Drag in brightfield microscopy images from adherent, suspension, scratch assay, or spheroid cultures. No code, no installation, no hardware.
CellOpsis segments every image in about five seconds, then shows you the masks it drew so you can check the result before you trust it.
Take away counts, confluency, and viability as CSV alongside labelled images, ready for your lab record, your report, or your automation stack.
image_id,cell_count,viability,model
suspension-demo-01,2416,0.91,suspension-v1
adherent-demo-01,1284,,adherent-v1
Download example CSV
Drag any divider to compare the raw image with what CellOpsis sees.
Track confluency, growth, and repeatable cell counts over time from label-free images.
Segmented
Raw
Detect suspended cells in dense brightfield images and determine their viability over time.
Segmented
Raw
Follow wound closure and treatment response across timepoints.
Segmented
Raw
Track spheroid growth, structure, and response over time in organoid and aggregate studies.
Segmented
Raw
If your assay, imaging modality, or throughput does not fit the formats above, tell us what you are working on. We build and train custom models on your own data and fit them to the workflow your team already runs.
Our product
Fine-tune segmentation on your own images and it improves with every correction you make. No labelling thousands of frames, no scripting, no ML team.
Off-the-shelf models
Pre-trained models are awkward to set up and rarely tuned to your modality. Even running well, they miss the detail that matters on your specific images.
Counting by hand
Manual counts and threshold-tweaking do not scale, drift between operators, and quietly introduce bias into the numbers you publish.
An image is analysed in about five seconds. Work that used to eat an afternoon at the microscope comes back before you have left the bench.
It runs in the browser. There is nothing to install, nothing to configure, and no engineer needed to keep it running.
The same model reads every image the same way, so counts stay comparable between flasks, timepoints, and people in the lab.
Where the built-in models fall short on your images, train CellOpsis on your own data until it reads them the way you would.
Bring your imaging setup, your assay, and one awkward image. We will show you where CellOpsis fits, what it would take to tune a model to your cells, and where the time savings actually land.
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CellOpsis is VolkCell's AI-powered image analysis software for cell biology labs. It turns raw microscopy images into reliable insights in seconds, directly in the browser, with no code, no installation, and no hardware required.
CellOpsis reaches 95% accuracy in the majority of cases, analysing roughly one image every 5 seconds. Where the built-in models fall short on your images, you can train CellOpsis on your own data to reach that level of accuracy and higher.
CellOpsis supports adherent, suspension, scratch/wound (scratch assay), and spheroid cultures using a single model family, providing counts, confluency, and viability measurements.
CellOpsis Live has a free tier (up to 5 images per day). The Academic plan is free for verified academic users, with 1,000 images per month and custom model training. Enterprise plans, with image volume matched to your throughput plus workflow integrations and priority support, are available on request.
VolkCell is based in Edinburgh, United Kingdom.
Join us
Bring one microscopy image. CellOpsis will show you the fastest path from image to data.