The problem — A QC failure that reaches segmentation produces masks for cells that were never in focus. One that reaches profiling inflates or deflates the morphological signal of an entire plate edge. One that reaches a slide-level classifier embeds blurry tiles at full weight. The failure is invisible from the output alone — the result looks well-formed because the pipeline completed without error. QC exists to catch these failures before they propagate.
What it is / how it works — Fovea treats quality as four nested levels, as described in Image Quality Is Not One Number: image-space physics (focus, SNR, illumination uniformity), run/sample consistency (field-to-field drift, batch structure, registration stability), readout fidelity (segmentation accuracy, percent-replicating), and the operational decision (reportable / needs-review / reject). The QC stage primarily instruments the first two levels — per-FOV focus scoring, illumination drift detection, saturation flags, drift-over-time curves, bleaching profiles — because these are computable directly from images without ground truth. Metrics Reloaded (Maier-Hein et al.) grounds the readout-level checks: metric selection must reflect domain interest, not imaging convenience. The QC stage outputs a structured QC report that feeds the final QC-aware report.
Where it breaks — The general failure is instrumenting only the image-space level and reporting as if the readout level were clear. Sharp fields can sit inside a batch-confounded run; a per-FOV focus score cannot detect a well that saturated at 10 minutes. QC must also be modality-aware: for live-cell imaging, photobleaching and drift accumulate over time; for spatial omics, cycle registration error is the dominant failure; for digital pathology, tissue folds and pen marks are invisible to a sharpness metric. A QC stage that is not parameterized by modality will pass failures specific to that modality.