/histolab

name: histolab
description: Extracts and preprocesses whole-slide histology image tiles with Histolab. Use for WSI inspection, tissue masks, random/grid/score-based tile extraction, H&E stain normalization, and tile dataset preparation. For multiplexed imaging or deep learning inference pipelines, use pathml.
license: Apache-2.0 license
compatibility: Requires Python 3.8–3.11 and histolab 0.7.0 on Linux or macOS, plus native OpenSlide. Python 3.10 avoids scikit-image 0.19 source builds on macOS ARM. Optional pooch downloads samples; matplotlib plots results; large-image plus a tile source enables MPP extraction.
metadata:
  version: "1.5"
  skill-author: K-Dense Inc.
  last-reviewed: "2026-10-01"
  upstream-version: "0.7.0"

Histolab

When to use

Use Histolab to inspect WSI metadata, identify tissue, extract image tiles, and standardize H&E staining. Its masks and scores are image-processing heuristics; they do not diagnose cancer, count individual cells, or establish image quality.

Installation

Histolab 0.7.0 remains the latest published release as of the review date. Its [release constraints](https://github.com/histolab/histolab/blob/v0.7.0/pyproject.toml) require Python <3.12, NumPy <=1.24.4, scikit-image <0.19.4, SciPy <1.10.1, Pillow <11, and openslide-python 1.3.1. Keep this stack isolated from modern scientific environments. Windows is not supported by this Histolab release.

Install [native OpenSlide](https://openslide.org/download/) for your system, then create a dedicated environment (Python 3.10 was tested):

uv venv --python 3.10 .venv-histolab
uv pip install --python .venv-histolab/bin/python 'histolab==0.7.0' pooch matplotlib
.venv-histolab/bin/python -c 'import openslide; print(openslide.__library_version__)'

On macOS with Homebrew, `brew install openslide` installs the native library. If the older Python binding cannot find it, launch Python with the library path set immediately before Python starts:

env DYLD_FALLBACK_LIBRARY_PATH="$(brew --prefix openslide)/lib" .venv-histolab/bin/python -c 'import openslide; print(openslide.__library_version__)'

`pooch` is optional for remote examples. Start with a local slide or the tiny bundled `cmu_small_region` sample; other sample functions may download hundreds of megabytes. Exact `mpp` extraction also needs `large-image` and a matching source plugin; see [slide management](references/slide_management.md).

Workflow

1. Inspect `slide.dimensions`, `slide.levels` (a list), and `slide.level_dimensions(level)` (a method). Check both MPP axes in metadata. 2. Select physical field of view and pixel resolution; level numbers are not interchangeable across scanners. Preserve level-0 coordinate bounds. 3. Choose `TissueMask` for all tissue sections or `BiggestTissueBoxMask` for the largest section's bounding box. Inspect the mask at its actual resolution. 4. Configure a tiler and preview with the **same mask** passed to extraction. Preview methods return a Pillow image; save or display that return value. 5. Extract into a distinct per-slide/per-strategy directory. Count saved files, inspect representative tiles, and retain parameters, source IDs and QC flags. 6. Split datasets by patient before training/validation/test tile assignment. Fit stain normalization targets on training data only and validate on held-out scanners. A seed reproduces sampling; it does not prevent patient leakage.

Quick start

Illustrative for a user-provided slide; the same API path is tested with small local fixtures. `n_tiles` is an upper bound, not a promise of 100 valid tiles.

from pathlib import Path
from histolab.slide import Slide
from histolab.masks import TissueMask
from histolab.tiler import RandomTiler

output = Path("output/random_tiles")
output.mkdir(parents=True, exist_ok=True)
slide = Slide("slide.svs", processed_path=output)
mask = TissueMask()
slide.locate_mask(mask).save(output / "mask_preview.png")

tiler = RandomTiler(
    tile_size=(512, 512), n_tiles=100, level=0, seed=42,
    check_tissue=True, tissue_percent=80.0, prefix="random_",
)
tiler.locate_tiles(slide, extraction_mask=mask).save(output / "tile_preview.png")
tiler.extract(slide, extraction_mask=mask)
print("[OK] Saved tiles:", len(list(output.glob("random_tile_*.png"))))

`extraction_mask` belongs to `extract()` and `locate_tiles()`, not to the tiler constructor. `locate_tiles()` has no `n_tiles` argument. Previewing runs tile selection again, so it may be expensive; use a separate small tiler for initial exploration, then preview the final configuration before committing a large run.

Choose a strategy

| Tiler | Selection | Important limitation | | --- | --- | --- | | `RandomTiler` | Seeded sampling, at most `n_tiles`, up to `max_iter` attempts | May overlap, repeat, or miss rare structures | | `GridTiler` | Grid within the extraction mask | Boundary tiles and tissue checks can leave gaps | | `ScoreTiler` | Scores all eligible grid candidates; saves top `n_tiles` | Lower output count does not avoid scoring all candidates |

For grids, stride in each axis is tile size minus `pixel_overlap`; positive values must be smaller than both tile dimensions. Negative overlap leaves gaps. `ScoreTiler(n_tiles=0)` saves all eligible ranked tiles.

Nuclei and cellularity scores estimate stain-derived area fractions. They are not calibrated tumor probabilities or blur/focus scores. Score reports contain exactly `filename,score,scaled_score`; record coordinate bounds and physical resolution separately. Equal raw scores can make `scaled_score` undefined in 0.7.0, so inspect raw scores and finiteness before plotting or comparing them.

Troubleshooting and scientific checks

• **No/few tiles:** inspect mask and output counts, dimensions, level, and

`max_iter`. Lowering `tissue_percent` relaxes QC; validate the added tiles.

• **Missing sections:** the default biggest-region box excludes other sections.

Pass