Light-Sheet 3D/4D: a pipeline architecture for trustworthy imaging.
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
Gentle volume imaging
A thin plane of light excites only the plane being imaged, so whole volumes can be captured quickly and repeatedly, at far lower phototoxicity than point-scanning approaches.
THE HARD PART
Beyond the render
The geometry is settled; the difficulty is everything after it — fusing views, segmenting deep tissue and tracking lineages across enormous time-lapse volumes.
GET HELP WITH
Volumetric pipelines
Ingest, multi-view fusion, deep segmentation and lineage tracking, designed together so the pipeline can absorb what the instrument produces overnight.
RELATED
Storage that keeps up
Chunked, multiscale storage is an architecture decision, not a housekeeping detail: sequential file reads cannot keep pace with volumetric acquisition.
Pipeline backbone — reshapes per modality. Select a module for its sub-steps, tools, and deliverable.
Light-Sheet 3D/4D
High confidenceIngest
Ingest terabyte-scale light-sheet volumes, multi-view/tile/angle acquisitions and 4D time series into chunked, streamable, multiscale data structures. The goal is to preserve acquisition geometry (view angles, tile transforms, voxel calibration, time-lapse index) intact while enabling distributed registration, deconvolution and downstream analysis without loading whole volumes into RAM.
Operations
- Dataset discoveryIndex views, angles, tiles, channels, z-planes and timepoints from raw microscope exports (CZI, IMS, HDF5/XML, TIFF series).
- Axis and scale harmonizationNormalize axes to TCZYX order; persist physical voxel scales (XYZ in micrometers) and time intervals.
- Deskewing (lattice light-sheet)optionalCorrect the oblique scan angle of lattice/diSPIM acquisitions to produce orthogonal volumes; applied when stage-scan geometry requires it.
- Chunk planningChoose Zarr/N5 chunk shapes matched to downstream workloads (registration, deconvolution, streaming to viewer).
- Streaming conversionWrite OME-Zarr or BDV/N5-compatible stores with multiscale pyramids without loading full volumes into memory.
- Checksum and integrity validationCompute per-chunk checksums and validate file completeness against the expected acquisition manifest.
Inputs
- raw multi-view/multi-tile 3D/4D light-sheet volumesCZI · IMS · TIFF series · HDF5/XML · OME-TIFF · Imaris HDF5 · KLB
Outputs
- streamed multiscale volume storeOME-Zarr · N5 · HDF5/XML (BDV)
- volume inventory with checksumsYAML · Parquet · JSON
QC checks
- Volume completeness — Verify all expected views, tiles, channels and timepoints are present; flag missing planes or truncated files.
- File integrity (checksums) — Validate per-chunk MD5/Blake2 checksums against acquisition manifest; detect partial writes or corruption.
- Voxel size consistency — Check that physical voxel sizes are consistent across views and match instrument metadata; flag >5% discrepancy.
- Volume size sanity — Confirm reported volume dimensions (XYZ × T × C × views) match file byte-sizes within tolerance.
Failure modes it guards against
- Missing or truncated files — Verify file count and sizes against acquisition manifest before proceeding; fail closed.
- Incorrect voxel calibration — Cross-check OME-XML metadata with instrument log; require explicit unit confirmation.
- Incorrect axis order (XYZ vs ZYX) — Enforce TCZYX normalization at ingest; add axis-label unit tests.
- Memory blowup from non-chunked reads — Always use chunked/streaming reads (zarr, BDV); never load full volume into RAM.
- Deskew not applied to lattice acquisitions — Detect scan geometry from metadata; auto-apply deskew for LLS/diSPIM data.
Tools (11)
Read proprietary and OME microscopy files (CZI, IMS, ND2, LIF, TIFF); preserve vendor metadata as OME-XML.
HDF5/XML and N5 data model and streaming viewer for large multi-view light-sheet datasets.
Convert, validate and write OME-Zarr/OME-NGFF datasets; multiscale pyramid generation.
- zarrLibrary · MIT
Chunked N-dimensional array storage backend used by OME-Zarr; supports local, S3, GCS.
- tifffileLibrary · BSD-3-Clause
Read/write TIFF, BigTIFF, OME-TIFF, and pyramidal image variants; fast Python-native.
- bioioOSS · BSD-3-Clause
Plugin-based Python image reader supporting OME-TIFF, CZI, ND2, OME-Zarr; successor to AICSImageIO.
- bfioOSS · MIT
Python interface to Bio-Formats for reading/writing OME-TIFF and OME-Zarr without requiring full Java runtime.
- FractalOSS · BSD-3-Clause
Distributed bioimage workflow orchestration system centered on OME-Zarr; handles TB-scale plate ingestion.
- LLSpyOSS · BSD-3-Clause
Lattice light-sheet post-processing utility including deskewing, deconvolution and format conversion.
- Imaris File ConverterCommercial · commercial
Convert proprietary Imaris IMS format to OME-TIFF or other open formats.
- n5-ij (N5 ImageJ plugin)OSS · BSD-2-Clause
ImageJ/Fiji plugin for reading and writing N5 format volumes; used in BDV/BigStitcher ecosystem.
Metadata captured
| Field | Type | Example | Standard |
|---|---|---|---|
| pipeline_run_id | string | ls-ingest-2026-06-05-abc123 | — |
| software_versions | object | {'python': '3.12', 'bioio': '3.3.0', 'ome_zarr_py': '0.10.2', 'container': 'sha256:...'} | REMBI |
| input_dataset_uri | uri | s3://client-study/raw/embryo01.czi | OME-NGFF |
| output_dataset_uri | uri | s3://client-study/zarr/embryo01.zarr | OME-NGFF |
| view_count | int (count) | 4 | OME-XML |
| tile_grid_shape | array (count) | 3, 4 | OME-XML |
| voxel_size_xyz | array (micrometer) | 0.208, 0.208, 1.0 | OME-XML |
| z_step_micrometer | float (micrometer) | 1.0 | OME-XML |
| time_points | int (count) | 360 | OME-XML |
| time_interval | float (second) | 30.0 | OME-XML |
| channel_count | int (count) | 3 | OME-XML |
| chunk_shape | array (pixel) | 1, 1, 64, 512, 512 | OME-NGFF |
| deskew_applied | bool | False | — |
| acquisition_system | string | Zeiss Z.1 / Lightsheet 7 | REMBI |
You get
- Streamed OME-Zarr store · OME-Zarr
- Acquisition inventory · YAML/Parquet
References
- OME-NGFF specification spec
- REMBI: Recommended Metadata for Biological Images paper
- BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples paper
- Image processing tools for petabyte-scale light sheet microscopy data review
- Bio-Formats: a resource for interoperability of microscopy data docs