Live/Dead Cell Viability
Cell viability counting from live/dead fluorescence images
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
Viability percent
Size and shape measured for every detected object, then summarized across the population.
In your report: Per-object table, size distribution, and summary percentiles.
class
Live/dead ratio
Each detected feature assigned to a class using the agreed rule set, shown on the image.
In your report: Per-feature class, counts by class, and annotated overlays.
class
Live and dead counts
Each detected feature assigned to a class using the agreed rule set, shown on the image.
In your report: Per-feature class, counts by class, and annotated overlays.
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 dual-channel fluorescence images
- 2
Classify
AI separates live from dead signals
- 3
Calculate
Compute viability percentages per field
- 4
Export
Download viability ratios and cell-by-cell classifications
Built to handle your images
What the analysis does with the awkward cases — the reasons the numbers above hold up on real microscopy.
Dual-Channel Classification
Simultaneously process green (live) and red (dead) channels with automatic spectral separation
Debris Exclusion
AI distinguishes real cells from fluorescent debris, staining artifacts, and autofluorescence
Dose-Response Ready
Output structured for direct import into dose-response curve fitting tools like GraphPad Prism
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.
Live/Dead Cell Viability with ConductVision Image
Cell viability software that scores live/dead dual-fluorescence assays from images you upload. ConductVision simultaneously detects calcein AM (live) and ethidium homodimer (dead) signals, counts each population, and calculates viability percentages with automatic background correction and debris exclusion. It analyzes microscopy images rather than acting as a standalone viability counter instrument.
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 this a cell viability counter instrument?
No. ConductVision is software that scores viability from live/dead fluorescence images you upload. It does not replace your microscope or reader; it counts live and dead cells in the images those instruments produce.
How is percent viability calculated?
The software counts live-channel and dead-channel cells per field, excludes debris and autofluorescence, and reports viability as live cells divided by total cells. You can review the classification overlay before exporting.
Can I use the results for dose-response curves?
Yes. Viability ratios and per-cell classifications export in a structure that imports directly into curve-fitting tools such as GraphPad Prism.
Send a sample image and a measurement goal
We will show the closest ConductVision workflow and flag what needs custom validation for your images.
