Production microscopy pipelines
Microscopy AI solution needs more than models.
The verified, automated pipeline that ships as your product — raw microscopy in, decisions your team can defend out
From image to insight
The pipeline behind the insight
Raw images become biological insight through seven phases — each checked, traceable, and built to hold up.
QC: manifest validation report
QC: focus / illumination QC map
QC: mask confidence report
QC: % replicating + control summary
Model development & monitoring
The ML stages above are built and kept honest by a separate loop. Each model trains on the output of the step before the node it powers, then deploys back into it.
- Curate Evidence Sets
- Train & Stress-Test
- Validate & Calibrate
- Register & Deploy
- Monitor & Retrain
- Correction / restoration model → powers correct, trains on ingest output
- Segmentation model → powers segment, trains on correct output
- Representation / embedding model → powers profile, trains on segment output
- MoA / phenotype model → powers interpret, trains on validate output
Built to hold up
Engineered to hold up
A trusted result is more than a model output. It is a pipeline with controls, provenance, reproducibility, and a clear audit trail.
Composes the tools you already trust
We build around proven bioimaging tools — CellProfiler, Cellpose, StarDist, micro-SAM, BaSiCPy, CytoTable, Pycytominer — plus your own scripts and models. The result is a transparent system, not a black box.
Reproducible by design
Each run versions its code, parameters, containers, model weights, inputs, and outputs. Your team can reproduce a result months later or re-run the same configuration on new data.
QC is built into every stage
Every major step emits a checkable artifact: image QC, mask QC, profile QC, and metadata validation. Failures become visible early instead of quietly contaminating the final result.
Runs where your data lives
The same workflow can run on a workstation, on-prem HPC, or cloud infrastructure without rewriting the science. The pipeline adapts to the compute environment, not the other way around.
Scales from pilot to production
Start with a plate, batch, or slide; scale to larger screens with tiling, parallel execution, checkpointing, and resumable runs. The production path is designed from the beginning.
Built for large imaging data
High-content, whole-slide, time-lapse, and volumetric data can quickly become terabyte-scale. Pipelines stream, tile, chunk, and track data so analysis remains practical and auditable.
Tell us about your system.
Whether you’re running a core facility, a high-content screen, or a custom-built instrument — we’d like to hear about it.
Start a conversationOr reach us at hello@fovealab.com