Digital Pathology: 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
Whole-slide analysis
A scanner writes a multi-resolution image so large that everything downstream works on extracted tiles, which makes tiling strategy a pipeline decision.
RELATED
Encoders are not pipelines
Stain drift, tile quality and domain shift do not resolve themselves inside a frozen encoder, however strong the embedding it produces.
GET HELP WITH
Slide-scale workflows
Tiling, stain normalization, nuclear segmentation and slide-level readouts, with the quality checks that catch folded tissue and blurred tiles before scoring.
Pipeline backbone — reshapes per modality. Select a module for its sub-steps, tools, and deliverable.
Digital Pathology
High confidenceIngest
Read gigapixel pyramidal whole-slide images from vendor-specific formats (SVS, NDPI, MRXS, DICOM WSI, OME-TIFF), extract scanner and acquisition metadata, convert to chunked cloud-native representations, and generate a coordinate-indexed tile inventory that is analysis-ready. This stage is the foundation for all downstream QC, preprocessing and inference — correct tiling parameters and MPP calibration here prevent hard-to-detect spatial errors later.
Operations
- Format detection and validationDetect vendor format (Aperio SVS, Hamamatsu NDPI, 3DHISTECH MRXS, Leica SCN, DICOM WSI, OME-TIFF) and validate file integrity before opening.
- Pyramidal WSI loadingOpen the full image pyramid, enumerate resolution levels, verify level dimensions, and extract embedded scanner metadata (MPP, objective, ICC profile).
- Low-resolution tissue previewRender thumbnail at 1.25x or 2.5x level; compute foreground mask via Otsu thresholding on the LAB luminance channel to estimate tissue coverage.
- Tiling with overlapGenerate coordinate-indexed tiles at the chosen analysis magnification (typically 20x or 40x) with configurable stride and overlap; record tile coordinates in physical microns.
- Conversion to cloud-native formatoptionalWrite image pyramid to OME-Zarr or OME-TIFF with chunked storage; optionally convert to DICOM WSI for clinical interoperability.
- Manifest creationPersist slide, level and tile inventory (slide_id, MPP, objective, tile coordinates, pyramid levels) to Parquet or JSON.
Inputs
- Raw whole-slide images in vendor pyramidal formatsSVS · NDPI · MRXS · SCN · OME-TIFF · DICOM WSI · BIF · QPTIFF
Outputs
- Cloud-native tiled WSI pyramidOME-Zarr · OME-TIFF · DICOM WSI
- Tile coordinate manifestParquet · JSON
- Foreground tissue maskPNG · OME-Zarr
QC checks
- MPP calibration plausibility — Check that MPP is within expected range for stated magnification (e.g. 0.20–0.35 µm/px at 40x). Flag mismatches that indicate scanner metadata corruption.
- Pyramid completeness — Verify all expected resolution levels are present and readable; flag truncated pyramids.
- Tile integrity sampling — Randomly sample 1% of tiles and verify JPEG/JP2 decode success; detect partial file transfers or corruption.
- Foreground fraction floor — Flag slides with <10% tissue coverage as likely empty or grossly under-sectioned.
- Format / version compatibility — Confirm OpenSlide or chosen reader can parse the format without errors; log vendor format version.
Failure modes it guards against
- MPP absent or incorrect in vendor metadata — Cross-validate MPP against known tissue structures (e.g. red blood cell ~7 µm); fall back to calibration table by scanner model.
- Incomplete file transfer / truncated WSI — Checksum (MD5/SHA256) the source file before ingest; validate all pyramid levels open.
- Proprietary format not supported by reader — Maintain format compatibility matrix; fall back to Bio-Formats or HALO for edge cases.
- High tile overlap causing memory/storage explosion — Set overlap only as large as needed for the downstream model's receptive field.
- Scale/resolution confusion between pyramid levels — Record and validate analysis_level and corresponding MPP in manifest; assert before inference.
Tools (17)
Read vendor whole-slide image formats (SVS, NDPI, MRXS, SCN, BIF, QPTIFF) via a unified C library with Python bindings.
Read/write TIFF, BigTIFF, OME-TIFF and pyramidal TIFF variants; used for OME-TIFF I/O and low-level tile access.
Read/write OME-Zarr (NGFF) compliant multiscale image arrays; used to write analysis-ready pyramids to object storage.
- tiffslideOSS · BSD-3-Clause
Cloud-native drop-in replacement for openslide-python built on tifffile; reads WSI from object storage without local download.
- cucimOSS · Apache-2.0
GPU-accelerated WSI I/O (TIFF/SVS), NVJPEG decoding, and pathology image primitives for high-throughput tiling pipelines.
- Bio-FormatsOSS · GPL-2.0 (open) / commercial (OME)
Java library (with Python wrapper) supporting 150+ microscopy and pathology formats including Zeiss CZI, Leica LIF, and others beyond OpenSlide's scope.
- pyvipsLibrary · LGPL-2.1
Fast image processing library for large-image tiling, format conversion and pyramidal TIFF construction using libvips.
- large-imageOSS · Apache-2.0
Python library for serving and tiling large images including WSI; integrates OpenSlide, tifffile, DICOM and GDAL sources.
- pydicomOSS · MIT
Read DICOM WSI files and extract DICOM tags (pixel data, UIDs, scanner metadata).
- wsidicomOSS · Apache-2.0
High-level Python library specifically for DICOM WSI reading, tiling and writing; supports DICOM VL Whole Slide Microscopy Image.
- highdicomOSS · MIT
Python library for reading/writing DICOM Structured Reports (SR), Segmentation objects, and WSI; used for DICOM-compliant output.
- TIAToolboxOSS · BSD-3-Clause
End-to-end computational pathology toolbox with WSI reading, tiling, model inference and visualization.
- QuPathOSS · GPL-3.0
Cross-platform WSI viewer and analysis platform with scripting support (Groovy/Python); used for QC, annotation and tile export.
- PathMLOSS · Apache-2.0
Digital pathology preprocessing and ML pipeline toolkit; wraps OpenSlide and stain normalization for rapid pipeline construction.
- MONAIOSS · Apache-2.0
Medical imaging AI framework with MONAI Deploy for clinical WSI workflows and MONAI Label for interactive annotation.
- Indica Labs HALOCommercial · commercial
Commercial WSI analysis platform supporting 30+ scanner formats; used when vendor-certified format reading is required.
- VisiopharmCommercial · commercial
Commercial WSI analysis platform with certified scanner interfaces and regulatory-grade traceability.
Metadata captured
| Field | Type | Example | Standard |
|---|---|---|---|
| pipeline_run_id | string | dp-run-2026-06-06-a1b2c3 | — |
| software_versions | object | {'openslide': '4.0.0', 'tifffile': '2024.1.1', 'ome-zarr-py': '0.9.0', 'python': '3.11', 'container': 'sha256:abc'} | REMBI |
| slide_id | string | TCGA-A1-A0SD-01Z-00-DX1 | DICOM WSI |
| scanner_make_model | string | Leica Aperio AT2 | DICOM WSI |
| objective_magnification | float (x) | 40.0 | DICOM WSI |
| mpp | float (micrometer_per_pixel) | 0.2425 | DICOM WSI |
| pyramid_levels | int (count) | 5 | OME-NGFF |
| tile_size_px | int (pixel) | 512 | — |
| tile_overlap_px | int (pixel) | 64 | — |
| compression_codec | enum | JPEG | DICOM WSI |
| icc_color_profile | uri | s3://scanner-profiles/aperio-at2-sRGB.icc | DICOM WSI |
| slide_dimensions_px | array (pixel) | 98816, 75264 | OME-NGFF |
| foreground_fraction | float (fraction) | 0.68 | — |
| n_tiles_total | int (count) | 12480 | — |
| output_format | enum | ome-zarr | OME-NGFF |
| input_dataset_uri | uri | s3://client-study/raw/slide01.svs | — |
| output_dataset_uri | uri | s3://client-study/derived/slide01.zarr | OME-NGFF |
You get
- Cloud-native WSI pyramid · OME-Zarr
- Tile coordinate manifest · Parquet
- Slide ingest metadata record · JSON
References
- OpenSlide: A vendor-neutral software foundation for digital pathology paper
- OME-NGFF: A next-generation file format for expanding bioimaging data-access strategies spec
- DICOM Part 3: Information Object Definitions — VL Whole Slide Microscopy Image spec
- TIAToolbox: Computational Pathology Toolbox paper
- REMBI: Recommended Metadata for Biological Images paper
- CAMELYON dataset: 1399 H&E-stained sentinel lymph node sections benchmark