Automated Cell Counting Software
Precise cell counting using fluorescent markers
Example outputs shown for illustration. Numbers depend on your samples and protocol.
Results
Each measurement below comes from your own images, tied back to the annotated frame it came from, so results stay comparable across samples, locations, and conditions.
µm or count
Cell count
Size and shape measured for every detected object, then summarized across the population.
In your report: Per-object table, size distribution, and summary percentiles.
per area
Cell density
Findings placed in sample coordinates to reveal spatial patterns and local density.
In your report: Coordinate-referenced map, regional density, and representative images.
µm or count
Marker-positive fraction
Size and shape measured for every detected object, then summarized across the population.
In your report: Per-object table, size distribution, and summary percentiles.
Built around your image set
Use the calibrated images your team already collects, together with the locations you need to compare.
Each report includes
The artifacts your team receives, ready for review and archive.
Annotated image set
Original images with regions of interest, measurement points, masks, and finding overlays.
Measurement table
Location-indexed values, units, distributions, and QC flags in a structured export.
Method record
Calibration evidence, analysis settings, version history, and validation summary.
Confidence in every resultTraceable measurements, reviewed against your agreed reference method.
- Traceable scale
- The report records the image scale, calibration evidence, and a method-specific expanded measurement uncertainty. It does not use one product-wide accuracy number.
- Reference comparison
- The configured method is compared with your accepted reference method or reviewed annotations, and reports bias by measurement range and image condition.
- Detection performance
- Detections are evaluated against reviewed reference regions with precision, recall, and segmentation overlap, separated by the conditions that affect performance.
- Repeatability
- The locked protocol is rerun on the same inputs and on a defined repeat set. The review records variation from image acquisition, sampling, and analysis separately where possible.
From image to reviewed result
- 1
Upload
Load fluorescence microscopy images
- 2
Detect
AI identifies fluorescent cells automatically
- 3
Review
Verify overlay on original image
- 4
Export
Download counts, annotated images, methods
Built to handle your images
What the analysis does with the awkward cases — the reasons the numbers above hold up on real microscopy.
AI-Powered Detection
Neural network trained on diverse fluorescent markers handles overlapping cells, dim signals, and debris without manual tuning
Publication-Ready Outputs
Annotated images with detection overlays, summary statistics, and auto-generated methods paragraphs ready for manuscripts
Batch Processing at Scale
Process entire 96-well plate experiments in one run with consistent parameters locked across all images
Works with your existing tools
Related biology applications
Use these Life Science pages when the same image-analysis method needs to be evaluated in the context of an assay, marker panel, plate workflow, or biological endpoint.
Automated Cell Counting (Fluorescence) with ConductVision Image
Automated cell counting software that identifies and quantifies individual cells in fluorescence microscopy images using DAPI, Hoechst, GFP, and other common markers. ConductVision’s AI segmentation handles overlapping nuclei and varying intensity levels without manual thresholding, delivering reproducible counts across entire plate experiments. It analyzes images you upload from your existing microscope — it is software, not a benchtop cell counter or hemocytometer.
Not sure this is the right measurement?
Send a representative image and your measurement goal. A ConductVision scientist will confirm whether this is the right fit, or point you to the closer workflow, before you commit to a quote.
Questions & answers
The questions scientists ask before sending a sample set. If yours is not here, ask a scientist directly.
Is ConductVision an automated cell counter machine?
No. ConductVision is image-analysis software, not a benchtop instrument, hemocytometer, or flow-based counter. It counts cells from fluorescence microscopy images you have already captured and upload, so it works with whatever microscope or imager your lab already uses.
How do I count cells in a fluorescence microscopy image?
Upload the image, let the AI detect labeled cells, review the detection overlay on your original image, and export the count. You can lock the same settings and reuse them across every image in a plate or experiment so counts stay comparable.
Which fluorescent markers and stains are supported?
Common nuclear and cytoplasmic labels including DAPI, Hoechst, and GFP, plus other single-channel fluorescent markers. You choose which channel to count, and the software segments individual cells even where signal intensity varies across the field.
Can it count cells across a whole 96-well plate automatically?
Yes. Once you set counting parameters on one image, you can run the same locked settings across a full plate or an image folder in one batch, with per-image and aggregate counts in the export.
How is automated cell counting different from manual counting?
Automated counting applies the same rules to every image, which removes the observer-to-observer variability of manual counting and scales to large image sets. Because you review the overlay before exporting, you keep a visual check on every count rather than trusting a black box.
Send a sample image and a measurement goal
We will show the closest ConductVision workflow and flag what needs custom validation for your images.
