The intelligence layer for quantitative imaging.

01 · Agentic Imaging

Run, diagnose, and improve the system.

Use system context and bounded tools to prepare and monitor approved runs, investigate exceptions, and evaluate candidate changes while the Verified Pipeline remains the execution system of record.

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02 · Scientific Insight

See what changed, where, and under which conditions.

Turn released, evidence-bearing imaging measurements into condition, phenotype, temporal, and spatial analyses linked through stable identifiers to source images and experimental context.

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03 · Verified Pipeline

Keep evidence attached to every result.

Turn approved imaging methods into controlled execution with explicit operating conditions, stage-level QC, provenance, review, and change control.

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04 · Computational Imaging

Make complex signals measurable.

Design computation around the instrument, sample, and intended measurement—from calibration and reconstruction to segmentation, tracking, and quantitative features.

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05 · Adaptive Acquisition

Let the experiment respond in real time.

Connect live image analysis to bounded instrument control so acquisition can refocus, retarget, rescan, change sampling, or switch modes as the experiment changes.

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Verified Pipeline

Keep evidence attached to every result.

Turn approved imaging methods into controlled execution with explicit operating conditions, stage-level QC, provenance, review, and change control.

The hidden gap

A pipeline can run successfully and still be wrong.

A successful job proves execution, not that the measurement supports its intended use. The gap often appears here:

  • Same image, different numbers.

    Two analysis stacks can return different values while both report success. Software and versions are part of the measurement.

  • Validated once is not validated here.

    A method can pass its original tests and fail silently when instrument, sample, or data conditions drift.

    Read the Lab Note

  • Code alone does not reproduce a result.

    Code omits the environment, data, parameters, model versions, and intermediate outputs needed to reproduce a result.

Four different questions

Four questions every result must answer.

Reproducible

Can the process run again and return the same result?

Typical evidence: code, versions, environment, parameters.

Leaves open: correctness and scientific validity.

Traceable

Can we reconstruct what happened?

Typical evidence: provenance, lineage, intermediate artifacts, audit trail.

Leaves open: whether the result was appropriate.

Validated

Has the method been shown fit for its intended use, under stated conditions?

Typical evidence: representative data, controls, performance metrics, operating range, acceptance criteria.

Leaves open: whether this run stayed inside those conditions.

Verified

Does the evidence from this run show it met every requirement the validated method sets?

Typical evidence: approved inputs, operating conditions, QC outcomes, controls, provenance, review state.

Establishes: evidence for this result, beyond the method's.

We distinguish these because each answers a different question. Validation shows a method is fit for its purpose; verification shows this result was produced within it. A pipeline can be fully reproducible and still leave the final number unsupported.

Evidence chain

Every quantitative result has an evidence chain.

Keep versions, parameters, QC, controls, operating conditions, provenance, and review connected across six parts of the measurement system:

  1. 1. Measurement definition
  2. 2. Acquisition evidence
  3. 3. Data integrity
  4. 4. Model / transformation evidence
  5. 5. Measurement assurance
  6. 6. Claim evidence

A number without its evidence chain is harder to defend or improve.

Community foundationsInstrument QC, metadata, provenance, model reporting, and quantitative performance already have strong community foundations.

The operating loop

Define → Build → Verify → Operate → Improve

  1. DEFINE

    Measurement requirements and intended use become explicit.

  2. BUILD

    Instrument, data, computation, and models become one system.

  3. VERIFY

    Failure modes, representative validation, QC, and acceptance criteria become testable.

  4. OPERATE

    Every run carries versions, provenance, QC, and review.

  5. IMPROVE

    Drift and change trigger controlled evaluation and revalidation — without freezing the pipeline.

Where is your pipeline strong, fragile, or unsupported?

The Fovea Verified Pipeline Assessment maps acquisition to result, exposes hidden assumptions and silent failures, and identifies controls required before transfer or scale.

You receive a measurement map, evidence and risk gaps, missing controls, a target architecture, and a prioritized roadmap.

Evaluate your pipeline

Continue exploring

Follow the question from another angle.

  • Principle

    Separate verification from validation.

    Learn why repeatability and scientific support answer different questions.

    Read the Lab Note

  • Approach

    See one pipeline as a measurement system.

    Explore a representative worked example connecting pipeline stages, cross-cutting QC, and model development.

    Explore the approach

  • Application

    See evidence in context.

    Browse how pipeline responsibilities change across imaging programs.

    Browse applications

  • Partnership

    Strengthen an existing pipeline.

    Review the focused ways Fovea can assess and improve your system.

    View services