Knowledge Vault

A living vault of connected notes, methods, and pipeline knowledge.

Designed for both our team and our agentic systems to reason across microscopy, AI, and scientific software.

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By pipeline stage

Ingest

  • Data Standards and Scalable Storage — The OME data model and OME-NGFF/OME-Zarr exist because a microscopy image without a shared data model and a chunked, cloud-native layout is neither interpretable nor analyzable at scale. Format is not plumbing — it decides whether a petabyte dataset can be opened at all.
  • Why Interoperability Matters in Microscopy — A microscopy pipeline is always several tools in sequence, so the seams between them — file formats, metadata, coordinate conventions — decide whether the pipeline composes or quietly corrupts. Open standards like OME-Zarr/NGFF turn hand-offs from lossy conversions into clean interfaces, and avoid lock-in to any one vendor or tool.
  • Light-Sheet Microscopy — The Pipeline Problem Behind the Beautiful Volume — The rendered light-sheet volume is the easy part. Behind it is a petabyte-scale systems problem — ingest and chunking, deconvolution and destriping, stitching and registration, then tracking — and every stage is where the science silently degrades, not the final render.
  • Measure Where It Matters — Adaptive, uncertainty-driven acquisition treats the microscope as part of the model — spending photons and time only where the image is uncertain or the biology is happening, instead of scanning everything uniformly.

Metadata

  • Metadata & Provenance: The Run Manifest — A run manifest captures the provenance and acquisition metadata that turn a folder of images into a dataset you can trust, reproduce, and compare across runs.
  • Verification — A Pipeline You Can Re-Run — Verification is the engineering discipline of making a microscopy pipeline deterministic, version-pinned, and re-executable from a provenance record. Without it, every result is a one-off, and "we changed nothing" is unprovable.
  • Every Output Should Know Where It Came From — Provenance is a property of the artifact, not the pipeline — every mask, profile, and count matrix should carry the lineage that produced it. Where verification asks whether a run reproduces, provenance asks whether a single output, found alone, can still explain itself.
  • How to Make Imaging Data Ready for Reanalysis — Reanalysis-ready means a third party with no contact with the original lab can re-run, re-segment, or re-interpret the data correctly — which requires FAIR principles, complete metadata, and deposition in a public archive. The bar is reuse by a stranger, not retrieval by the author.

Quality Control

  • The QC-Aware Report — A QC-aware report doesn't just present a result — it surfaces the evidence that the result is trustworthy, structured by the four nested levels of quality and ending in an explicit decision. Its job is to make a number un-trustable on sight when it shouldn't be trusted.
  • Image Quality Is Not One Number — Fovea treats microscopy quality as four nested levels — image-space, run/sample, readout, and decision — because a metric at one level says nothing about the levels above it. Full-reference scalars like SSIM and PSNR measure only the first, yet teams report as if they measured the third.
  • The Challenge of Rare Events and Mutants — When the phenotype you care about appears in 10 cells out of 300,000, accuracy is meaningless, validation has almost no positives to learn from, and false discovery dominates. Rare-event detection is a class-imbalance and ground-truth-scarcity problem — best answered by adaptive acquisition that goes looking for the positives.
  • Uncertainty, Drift, and Failure Modes in Bioimage Analysis — Models fail silently when the data drifts away from what they were trained on — and an overconfident, miscalibrated model gives no warning. Trustworthy deployment needs calibrated uncertainty, explicit distribution-shift detection, and a way to flag failure when there is no ground truth to check against.
  • Verification and Validation Are Two Different Questions — Verification asks whether the pipeline was built right — deterministic, reproducible, correct to spec. Validation asks whether it is the right pipeline — outputs that are biologically true and fit for purpose. Microscopy makes both hard, and conflating them is how silently wrong results get shipped.

Preprocess

  • Cell Painting Pipelines — From Images to Phenotypic Profiles — A Cell Painting profile is the output of a long pipeline — illumination correction, segmentation, feature extraction, batch correction, then a reproducibility readout — and the number that matters (percent-replicating / mAP) lives only at the end. Every stage upstream can silently degrade it.
  • How to Prepare Microscopy Data for AI — Most of the work of applying AI to microscopy is data preparation — formatting, normalization, tiling, and label hygiene — and the single most consequential decision is a split that respects the non-independence of microscopy data. Get the split wrong and every downstream metric is leaked, not earned.
  • From Research Script to Production Pipeline — A notebook that worked once is not a pipeline, and a community model that scored well on its paper's data is not a validated component. Turning either into something deployable is the engineering work of pinning, wrapping, gating, and validating — most of which the original artifact deliberately skipped.
  • Illumination Correction Before You Trust a Profile — Uneven illumination silently biases every downstream feature. Correct it first, or your batch effects are really optics.

Segment / Register / Track

  • Choosing a Bioimage Analysis Pipeline — Off-the-Shelf vs Custom — Most microscopy questions are answered by composing existing, validated tools — and a custom pipeline is justified only when off-the-shelf options fail on a specific, identifiable axis. This note gives the decision criteria and the signals that you have genuinely crossed into custom territory.
  • Evaluating Segmentation Models in Real Microscopy Workflows — A segmenter's benchmark Dice rarely predicts its behavior in a real workflow. Instance counting, merge/split errors, and cross-instrument generalization are the quantities that decide whether the downstream readout is correct — and standard semantic overlap scores are blind to all three.
  • Ground Truth Is a Design Problem — Ground truth in microscopy is constructed, not given — every label is the output of an annotation protocol, an annotator, and a fusion rule, each with its own bias and variance. Treating it as a fixed answer key is how inter-rater noise and label bias get baked into every score computed against it.
  • Model Zoos for Bioimage Analysis — From a Zoo Model to a Validated Workflow — The BioImage Model Zoo and community packaging tools make pre-trained models shareable, runnable, and reproducible across tools — solving distribution and provenance. They do not solve fitness: a downloaded model is a component, and the path from a zoo entry to a trustworthy readout still runs through validation.
  • The Bioimage Analysis Ecosystem — Where Each Tool Fits — Bioimage analysis is not one tool but a landscape organized along two axes — interactive vs scriptable, and generalist vs specialist. Fiji, napari, CellProfiler, QuPath, ilastik, and the deep-learning segmenters each occupy a different cell of that grid, and choosing well means knowing which cell your problem lives in.
  • Validation Without a Ground Truth — Validation asks whether a microscopy pipeline's outputs are biologically true and fit for purpose — but biology rarely supplies a clean answer key. The discipline is choosing metrics that reflect the question, manufacturing ground truth honestly, and conditioning performance on the experiment.
  • Cellpose vs CellProfiler for Nuclei Segmentation — When a generalist deep model beats a tuned classical pipeline for nuclei — and when it does not.

Features / Embeddings

  • From Masks to Morphology: Features & Self-Supervised Embeddings — Once cells are segmented, you turn masks into numbers — either hand-engineered morphology features or learned embeddings. Each choice trades interpretability against the signal it can capture.
  • Foundation Models Are Not Pipelines — A foundation model is a powerful feature extractor, not a deployable measurement pipeline. It still needs ingestion, QC, preprocessing, confounder correction, validation, and provenance around it — and a frozen encoder makes the surrounding system more important, not less.

Batch / Domain Correction

Biological Readout

  • Why mAP, Dice, and SSIM Are Not Enough — Dice, IoU, mAP, and SSIM measure overlap or similarity against a reference — not whether the result supports the decision the experiment exists to make. The discipline is problem-aware metric selection: derive the metric from the question, not from what is easy to compute.
  • Why Microscopy Needs Verified Outputs — A measurement you cannot reproduce is not a measurement. Why verification is a first-class pipeline stage, not an afterthought.

Other notes

  • A Model Isn't the System — A trained model is a small fraction of a working measurement pipeline. Most of the code, and almost all of the risk, lives in the data, glue, configuration, and validation around it — which is why a model that scores well on a benchmark is not a result you can ship.
  • Why Fovea Lab Uses a Knowledge Graph — A recommendation is only as trustworthy as what it knows a tool can't do. Fovea represents tools, models, papers, and constraints as a graph with explicit negative edges — unsupported modalities, known failure modes, hard requirements — so the system can say "no" with a reason, not just rank positive matches.

By modality

Cell Painting 27Light-Sheet 3D/4D 20Digital Pathology 20Spatial Omics 17Live-Cell Tracking 4Organoids / 3D 1Particle Tracking 0Label-Free / QPI 0

As new clusters land, this grid fills out — the breadth of the system becomes visible at a glance.

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