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Skillsalterlab-histolab

alterlab-histolab

Design & creation

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Updated 10-15 19:56

About this skill

Histolab

Overview

Histolab is a Python library for processing whole slide images (WSI) in digital pathology. It automates tissue detection, extracts informative tiles from gigapixel images, and prepares datasets for deep learning pipelines. The library handles multiple WSI formats, implements sophisticated tissue segmentation, and provides flexible tile extraction strategies.

When to Use This Skill

Use histolab for lightweight WSI tile pipelines: tissue detection, building tile datasets for ML training, H&E stain handling, and quick tile-based analysis of histopathology slides. For advanced spatial proteomics, multiplexed imaging, or full deep-learning pathology pipelines, use alterlab-pathml instead.

Does NOT Trigger

Scenario Use Instead
End-to-end computational pathology, nucleus segmentation, multiplexed imaging (CODEX/Vectra) alterlab-pathml
Training the downstream classifier on the extracted tiles alterlab-pytorch-lightning
DICOM whole-slide or radiology images rather than SVS/NDPI alterlab-pydicom, alterlab-imaging-data-commons
Spatial transcriptomics on tissue sections alterlab-squidpy-spatial
Managing/versioning the slide collection itself alterlab-omero, alterlab-lamindb

Installation

# histolab 0.7.0 needs Python >= 3.8, < 3.12 and numpy 1.x — give it its own env
uv venv --python 3.11 .venv-histolab
uv pip install --python .venv-histolab "histolab==0.7.0" openslide-bin

Two constraints that bite in a 2026 toolchain:

  1. Pinned scientific stack. 0.7.0 (Feb 2024, still the latest release) requires numpy <= 1.24.4, scipy < 1.10.1, scikit-image < 0.19.4 and openslide-python 1.3.1. It cannot share an environment with a numpy 2.x / Python 3.12+ project — resolve this with a separate venv rather than by loosening pins.
  2. OpenSlide is a C library, not bundled with the wheel. The simplest fix is the openslide-bin wheel (prebuilt binaries); otherwise brew install openslide on macOS or apt install libopenslide0 on Debian/Ubuntu. Without it, import histolab.slide fails with Couldn't locate OpenSlide dylib.

The examples below target histolab 0.7.0; the API differs in older releases.

Core Workflow

  1. Load the slide with Slide(path, processed_path=...) and inspect dimensions/levels.
  2. Detect tissue with a mask (TissueMask or BiggestTissueBoxMask).
  3. Preview tile locations with tiler.locate_tiles(slide) before committing.
  4. Extract tiles with one of three tilers (Random/Grid/Score).

Minimal example:

from histolab.slide import Slide
from histolab.tiler import RandomTiler

slide = Slide("slide.svs", processed_path="output/")
# n_tiles, level, seed are CONSTRUCTOR args — not args to locate_tiles/extract.
tiler = RandomTiler(tile_size=(512, 512), n_tiles=100, level=0, seed=42)
tiler.locate_tiles(slide)   # preview locations on the thumbnail first
tiler.extract(slide)        # writes PNGs into processed_path

API gotcha (histolab 0.7.0): locate_tiles() and extract() take only slide, an optional extraction_mask, and logging/styling kwargs — they do not accept n_tiles. Set n_tiles (and seed, level, tile_size, check_tissue, tissue_percent) on the tiler constructor. The extraction_mask is passed to extract()/locate_tiles(), never to the constructor.

Full copy-pasteable pipelines (quick start, 5 end-to-end workflows, and per-capability examples) live in references/workflows.md.

Core Capabilities

1. Slide Management

Load, inspect, and work with WSI files (SVS, TIFF, NDPI, etc.): access metadata (dimensions, magnification, properties), generate thumbnails, and work with pyramidal/multi-level structures. Key class: Slide.

See references/slide_management.md for slide initialization, built-in sample datasets (prostate_tissue, ovarian_tissue, breast_tissue, heart_tissue, aorta_tissue, plus pen-marked and IHC samples), pyramid levels, and multi-slide processing.

2. Tissue Detection and Masks

Automatically identify tissue regions and filter background/artifacts. Key classes: TissueMask (all tissue regions), BiggestTissueBoxMask (bounding box of largest region — the default), and BinaryMask (base class for custom masks).

Choosing a mask:

  • TissueMask: multiple tissue sections, comprehensive analysis
  • BiggestTissueBoxMask: single main section, exclude artifacts (default)
  • Custom BinaryMask: specific ROI, exclude annotations, custom segmentation

See references/tissue_masks.md for how detection filters work, visualizing masks with locate_mask(), and custom rectangular / annotation-exclusion masks.

3. Tile Extraction

Extract smaller regions from large WSI using one of three strategies:

  • RandomTiler — fixed number of randomly positioned tiles. Best for sampling diverse regions, exploration, training data. Key params: n_tiles, seed.
  • GridTiler — systematic grid across tissue. Best for complete coverage, spatial analysis, reconstruction. Key param: pixel_overlap.
  • ScoreTiler — top-ranked tiles by scoring function. Best for informative regions, quality-driven selection. Key param: scorer (NucleiScorer, CellularityScorer, custom).

Common parameters: tile_size, level (0 = highest res), check_tissue, tissue_percent (default 80%), extraction_mask. Always preview with locate_tiles() before extracting.

See references/tile_extraction.md for scorers, reporting, and advanced (multi-level, hierarchical) extraction patterns.

4. Filters and Preprocessing

Apply image-processing filters for tissue detection, QC, and preprocessing:

  • Image filters — RgbToGrayscale, RgbToHsv, RgbToHed, OtsuThreshold, Invert, StretchContrast, HistogramEqualization, Lambda.
  • Morphological filters — BinaryDilation, BinaryErosion, BinaryOpening, BinaryClosing, RemoveSmallObjects, RemoveSmallHoles.
  • Composition — Compose (in histolab.filters.image_filters) chains filters into pipelines. Pass custom filters to a mask as positional varargs: TissueMask(RgbToGrayscale(), OtsuThreshold(), ...).

See references/filters_preprocessing.md for filter chaining, common pipelines (tissue detection, pen removal, nuclei enhancement), and QC filters.

5. Visualization

Display slides, masks, tile locations, and extraction quality: thumbnails, mask overlays via locate_mask(), tile-location previews via locate_tiles(), tile mosaics, and score distributions.

See references/visualization.md for mosaics, quality-assessment plots, multi-slide comparison, and exporting high-resolution figures / PDF reports.

Reference Index

  • references/workflows.md — quick start, per-capability examples, and 5 end-to-end worked workflows (exploratory, grid, score-driven, multi-slide, custom tissue detection).
  • references/slide_management.md — loading/inspecting slides, sample datasets, pyramid levels, multi-slide processing.
  • references/tissue_masks.md — TissueMask/BiggestTissueBoxMask/BinaryMask, custom masks, mask visualization and integration.
  • references/tile_extraction.md — Random/Grid/Score tiler comparison, scorers, CSV reporting, advanced extraction patterns.
  • references/filters_preprocessing.md — image + morphological filters, filter composition, preprocessing pipelines, QC filters.
  • references/visualization.md — thumbnails, mask/tile previews, mosaics, quality plots, figure export.
  • references/best_practices.md — best practices, common use cases, and troubleshooting (no tiles, background tiles, slow extraction, artifacts).

Load the specific reference file you need for detailed implementation guidance, troubleshooting, or advanced features.

Part of the AlterLab Academic Skills suite.