Slide quantification
Run the full computational pathology pipeline on a tissue slide — tile generation, tissue detection, cell segmentation, and spatial quantification with interactive viewer review.
Research question
What is the tissue composition of this slide, how many cells are detected, and what spatial metrics characterize the tissue architecture?
Who this is for
- Researchers new to computational pathology needing a reproducible end-to-end workflow
- Labs migrating from QuPath or Fiji scripts to a web-based pipeline with run provenance
- Core facilities processing region images or whole-slide scans at scale
Data requirements
| Data | Required | Purpose |
|---|---|---|
| Image file | Yes | PNG, JPEG, TIFF, OME-TIFF, or WSI formats (SVS, NDPI, MRXS) |
| Modality and stain type | Recommended | Metadata for study organization and QC context |
| OpenSlide (for WSI) | Local env | Required for SVS/NDPI/MRXS decoding |
For local smoke tests, use a PNG region image or the synthetic fixture from Sample data.
Workflow
Create study → Upload slide → Generate tiles → Tissue detect → Segment cells → Spatial quantification
Step 1 — Create study and upload
Create a pathology study and upload a slide from the study home page. Select modality (whole-slide image or region image) and stain type (H&E, IHC, IF).
Step 2 — Generate tiles
On the slide detail page, click Generate tiles. Wait for the job to complete — the slide viewer loads with pan/zoom tile navigation. Tiles are required before segmentation on large slides.
Step 3 — Tissue detection
Click Detect tissue. Review tissue fraction, focus score, and detected regions in the Tissue QC panel. A tissue mask overlay appears in the slide viewer.
Step 4 — Segment cells
Click Segment cells (requires tiles). Review cell count, model name, and mean area in the Segmentation Summary panel. Cell overlay and the detected-cell table populate in the viewer.
Available models are threshold_baseline (connected components) and watershed (splits touching nuclei via distance-transform watershed); Cellpose and StarDist deep-learning integration is planned but not yet available.
For IHC slides, optionally run Color deconvolution first to separate the Hematoxylin/DAB stain channels and get a DAB-positive area fraction. After segmentation, you can also train an Object classifier from annotation-labeled regions to assign a class to every cell beyond the default morphology-based label.
Step 5 — Spatial quantification
Click Spatial quantification (requires segmentation). Review:
- Overall cell density
- Infiltration phenotype summary
- Nearest-neighbor distance statistics
- Region-level metrics
Export CSV and JSON artifacts from the metrics panel. To hand the processed slide off to QuPath, Fiji, or OMERO, run Export OME-TIFF for a pyramidal, self-describing file with pixel-size metadata.
Expected outputs
| Artifact | Contents |
|---|---|
| Tile manifest | Multi-level pyramid for web viewing |
tissue_mask.png | Binary tissue foreground |
cell_overlay.png | Segmentation visualization |
cells.json | Cell centroids, areas, confidence scores |
density_heatmap.png | Spatial density map |
cell_spatial_metrics.csv | Per-cell spatial measurements |
region_metrics.json | Compartment-level statistics |
Typical paths
| Slide type | Notes |
|---|---|
| Region PNG/JPEG | Fastest path for demos and algorithm validation |
| OME-TIFF region scan | Standard pathology format with embedded metadata |
| Full WSI (40× H&E) | Tile-first architecture; requires OpenSlide locally |