| name | alterlab-histolab |
| description | Extract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction, and image/morphological filters for H&E preprocessing. Use when the user needs lightweight WSI slide preprocessing — building tile datasets for ML training, tissue segmentation, or quick tile-based inspection of histopathology slides. For end-to-end computational-pathology, deep-learning model training, nucleus segmentation, or multiplexed/spatial-proteomics (CODEX, Vectra) pipelines prefer alterlab-pathml instead. Part of the AlterLab Academic Skills suite. |
| license | Apache-2.0 |
| allowed-tools | Read Write Edit Bash(python:*) Bash(uv:*) |
| compatibility | Self-contained — runs under `uv run python` with the skill's Python package installed; no API key or account required. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0"} |
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 pathml instead.
Installation
uv pip install "histolab==0.7.0"
histolab wraps the OpenSlide C library, which is not bundled with the pip
package. On macOS install it with brew install openslide; without it, any
import histolab.slide fails with Couldn't locate OpenSlide dylib. The
examples below are pinned to histolab 0.7.0; the API differs in older releases.
Core Workflow
- Load the slide with
Slide(path, processed_path=...) and inspect dimensions/levels.
- Detect tissue with a mask (
TissueMask or BiggestTissueBoxMask).
- Preview tile locations with
tiler.locate_tiles(slide) before committing.
- 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/")
tiler = RandomTiler(tile_size=(512, 512), n_tiles=100, level=0, seed=42)
tiler.locate_tiles(slide)
tiler.extract(slide)
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 analysisBiggestTissueBoxMask: 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.
