Built around the question, not just the image.
01 · Phenotypic Discovery
Find meaningful biological responses across large experimental spaces.
High-content experiments generate more images than scientists can examine directly, but scale alone does not produce insight. The analysis must preserve the relationship between perturbations, controls, cells, wells, plates, and batches while turning millions of individual observations into comparable phenotypic profiles.
Fovea Lab supports the full path from image correction and cellular segmentation to feature extraction, representation learning, batch correction, and biological readout. Each result remains connected to the conditions and quality checks that produced it, allowing researchers to distinguish reproducible phenotypes from technical variation and investigate rare responses that would otherwise disappear inside population averages.
02 · Space & Time
Follow biological systems as they form, move, and change.
Volumetric and time-resolved imaging reveals processes that cannot be understood from a single field or endpoint. Cells migrate, tissues reorganize, structures develop, and rare events emerge across large image volumes. Extracting those events requires more than processing individual frames: the analysis must remain consistent across depth, time, scale, and changing image quality.
Fovea Lab brings acquisition context, restoration, segmentation, registration, tracking, and quantitative measurement into one inspectable workflow. Researchers can follow an observation from a rendered volume or trajectory back to the source data and processing decisions that created it, while quality checks expose where depth, motion, density, or signal loss may have changed the reliability of the result.
03 · Tissue & Spatial Biology
Connect cellular organization with molecular and pathological context.
Tissue images contain information at several scales at once: cellular morphology, local neighborhoods, larger anatomical structures, molecular measurements, and clinically meaningful regions. These signals are often processed in separate systems, making it difficult to understand how a final classification, spatial association, or tissue-level measurement was formed.
Fovea Lab structures image processing, registration, object detection, spatial measurement, and model interpretation as one traceable analysis. Imaging and molecular information can remain aligned through each transformation, while provenance, cohort-aware validation, and reviewable outputs help researchers examine whether a finding is consistent across samples, scanners, staining conditions, and experimental sites.
Pipeline architectures, module by module.
WHAT IT IS
Image-based profiling
A fixed dye panel turns cell shape into a comparable profile, so treatments can be ranked by phenotype rather than by a single readout.
GET HELP WITH
Screen-scale profiling
Plate-scale profiling built end to end: ingest, correction, segmentation, feature extraction and hit triage, with the batch effects handled rather than hidden.
QUALITY GATE
When replicates disagree
If replicate wells do not agree with each other, something upstream has already failed and the hit list cannot be trusted.
RELATED
Batch effects
Plate-to-plate drift is a quality gate, not a footnote. Removing it too aggressively erases the biology the screen was run to find.
Pipeline backbone — reshapes per modality. Select a module for its sub-steps, tools, and deliverable.
Cell Painting
High confidenceIngest
Read raw 5–6 channel HCS plate images from proprietary microscope formats and convert them into harmonized, chunked, plate-aware OME-Zarr stores. This stage preserves the full plate/well/site/ channel hierarchy so every downstream per-cell measurement remains traceable to its acquisition context, and validates completeness of the well-site matrix before any analysis begins.
Operations
- Vendor file discoveryScan plate directories and companion metadata files for expected wells, sites and channels; detect missing fields.
- Format detection and reader selectionIdentify vendor format (OME-TIFF, CZI, ND2, FLEX, Opera Phenix/Harmony, LIF, etc.) and select the appropriate Bio-Formats or Python reader.
- Channel-map harmonizationMap vendor-specific channel names/indices to the canonical Cell Painting biological channel labels (DNA, ER, RNA, AGP, Mito, optional Brightfield).
- Chunked OME-Zarr conversionWrite analysis-ready OME-Zarr with multiscales, per-channel metadata, plate/well/site axes and zattrs compliant with OME-NGFF v0.4+.
- Checksum and integrity validationVerify file readability, pixel-dimension consistency, missing-well accounting and SHA-256 checksums; fail closed on incomplete data.
- GPU-accelerated decode (optional)optionalUse cucim or CLIJ2 for GPU-accelerated TIFF/Zarr I/O when throughput bottleneck is on disk-to-memory transfer at scale.
Inputs
- raw 5–6 channel HCS plate images (multiwell, multisite, multichannel)OME-TIFF · CZI · ND2 · FLEX · LIF · TIFF · PerkinElmer Opera Phenix exports
- plate acquisition folders, companion XML and optional plate mapCSV · XLSX · JSON · XML
Outputs
- harmonized plate image store with multiscales and channel metadataOME-Zarr · OME-TIFF
- ingest manifest and file inventory with per-well statusYAML · JSON · Parquet
QC checks
- Well/site completeness — Verify the ingested well-site matrix matches the expected plate layout; report missing and extra sites.
- Channel count per site — Confirm every site has exactly the expected number of channels (5 for standard Cell Painting, 6 with brightfield); fail on channel dropout.
- Image dimension consistency — Verify all sites have identical pixel dimensions (X×Y) and bit depth; flag size mismatches that would break tiling.
- Pixel intensity saturation check at ingest — Compute fraction of saturated pixels (at detector max) per channel per site; high saturation at ingest flags overexposure before correction.
- Checksum integrity — SHA-256 hash verification of raw input files to confirm lossless transfer and bit-identical reads.
Failure modes it guards against
- Missing wells or sites (plate dropout) — Fail closed with per-well status table; require minimum fill fraction before proceeding.
- Channel name mismatch — Require explicit channel-map validation step; cross-check against JUMP/Cimini 2023 canonical channel order.
- Partial file reads (truncated images) — Validate pixel-dimension and checksum on every file; quarantine and report failures.
- Vendor metadata loss — Extract OME-XML companion or embedded metadata at ingest; fall back to plate-level config YAML if vendor metadata absent.
Tools (13)
Read >150 proprietary and OME microscopy formats; preserve vendor metadata and OME-XML companion.
Python image reader/writer for microscopy formats and OME-Zarr/TIFF workflows.
Convert, validate and write OME-Zarr/OME-NGFF datasets; generate multiscales and plate attrs.
- bioioOSS · BSD-3-Clause
Successor ecosystem to AICSImageIO; modular plugin-based bioimage I/O for OME-Zarr and microscopy files.
- bfioOSS · MIT
Blocked, tiled bioimage file I/O wrapping Bio-Formats for scalable, chunk-parallel workflows.
- zarrLibrary · MIT
Chunked N-dimensional array storage backend for OME-Zarr; supports local, S3, GCS, Azure.
- tifffileLibrary · BSD-3-Clause
Read/write TIFF, BigTIFF, OME-TIFF and pyramidal variants; zero-copy mmap for large stacks.
- FractalOSS · BSD-3-Clause
Distributed bioimage workflow orchestration centered on OME-Zarr plates; manages Cell Painting ingest tasks at scale.
- OMEROOSS · AGPL-3.0
Image data management server; centralizes storage, metadata, access control and rendering for bioimaging.
- cucimOSS · Apache-2.0
GPU-accelerated image I/O and processing (TIFF, Zarr); NVIDIA RAPIDS ecosystem.
- Revvity HarmonyCommercial · commercial
Vendor-native analysis platform for Opera Phenix/Phenix Plus; reads proprietary plate exports directly.
- MetaXpressCommercial · commercial
Molecular Devices HCS image acquisition and analysis; native reader for ImageXpress plate formats.
- Fiji (ImageJ)OSS · GPL-2.0-or-later
Macro-based batch scripting for format conversion, metadata extraction and preliminary QC before OME-Zarr conversion.
Metadata captured
| Field | Type | Example | Standard |
|---|---|---|---|
| plate_id | string | BR00117035 | OME-NGFF |
| well_id | string | B03 | OME-NGFF |
| site_id | string | s4 | OME-NGFF |
| channel_map | object | {'DNA': 'C1', 'ER': 'C2', 'RNA': 'C3', 'AGP': 'C4', 'Mito': 'C5'} | OME-XML |
| pixel_size_xy | float (micrometer/pixel) | 0.65 | OME-XML |
| microscope_model | string | Opera Phenix Plus | REMBI |
| objective_na | float | 0.75 | OME-XML |
| bit_depth | int (bit) | 16 | OME-XML |
| acquisition_software_version | string | Harmony 5.3 | REMBI |
| exposure_times_ms | object (millisecond) | {'DNA': 100, 'ER': 150, 'RNA': 200, 'AGP': 300, 'Mito': 200} | OME-XML |
| binning | int | 1 | OME-XML |
| wells_expected_vs_found | object | {'expected': 384, 'found': 382, 'missing': ['P24', 'A01']} | — |
| ingest_checksum_sha256 | string | a3f2...c9 | — |
| zarr_chunk_shape | array (pixels) | 1, 1, 1, 1024, 1024 | OME-NGFF |
| software_versions | object | {'bioformats': '7.x', 'ome-zarr-py': '0.9.x', 'python': '3.11', 'container': 'sha256:...'} | REMBI |
You get
- Plate OME-Zarr · OME-Zarr
- Ingest manifest · Parquet/YAML
References
- OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies paper
- OME-NGFF specification spec
- REMBI: Recommended Metadata for Biological Images paper
- Cell Painting, a high-content image-based assay for morphological profiling (Bray et al. 2016) paper
- Image-based Profiling Handbook docs
- JUMP Cell Painting dataset — Three million images (Nature Methods 2024) dataset