histolab-wsi-processing
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
npx skills add BioTender-max/awesome-bio-agent-skills --skill histolab-wsi-processing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
The skill performs WSI processing by automating tissue detection, tile extraction using multiple strategies (random, grid, score-based), and configurable filter pipelines for preprocessing in H&E or IHC contexts. It supports multiple WSI formats via OpenSlide and integrates with PathML for multiplexed imaging workflows.
How it works
- Uses the Histolab library to load WSIs (supported formats include SVS, TIFF, NDPI, and others via OpenSlide).
- Provides tile extraction through three strategies: RandomTiler, GridTiler, and ScoreTiler, each configurable with tile_size, level, tissue checks, and optional seeds.
- Supplies tissue detection via TissueMask and other BinaryMask subclasses; can visualize masks on slide thumbnails.
- Enables image and morphological filtering through a Compose pipeline, producing custom masks for tissue extraction.
- Demonstrates usage of tilers with extraction calls (e.g., .extract(slide)) and optional reporting (e.g., report_path in extract).
- Includes guidance on WSI pyramids, level selection, and the interaction between masks and tilers (default BiggetTissueBoxMask vs TissueMask).
When to use it
- For extracting tiles from WSIs to prepare datasets for deep learning.
- When detecting tissue regions and filtering background/artifacts in histology slides.
- To build preprocessing pipelines for H&E or IHC slides and generate quality-controlled tile datasets.
- For batch tile extraction across slide collections with consistent parameters.
- When preparing data for raw slide access via OpenSlide or multiplexed imaging via pathml.
What it can touch
- Uses Python packages: histolab (and OpenSlide bindings).
- Interacts with Slide, tiler classes (RandomTiler, GridTiler, ScoreTiler), and masks (TissueMask, BinaryMask subclasses).
- Utilizes filter modules (image_filters, morphological_filters, compositions) to build custom pipelines.
- Accesses tiles via tiler.extract and can produce a CSV/report when provided (e.g., report_path).
Caveats
- Requires OpenSlide C library installed separately as a system dependency.
- Supported formats rely on OpenSlide compatibility.
- The description notes usage with PathML for multiplexed imaging, implying an orchestration with additional tools for advanced workflows.
# Histolab WSI Processing ## 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 using multiple strategies, and provides composable filter pipelines for preprocessing. The library handles SVS, TIFF, NDPI, and other WSI formats via OpenSlide. ## When to Use - Extracting tiles from whole slide images for deep learning model training - Detecting tissue regions and filtering background/artifacts in histopathology slides - Building preprocessing pipelines for H&E or IHC stained tissue sections - Creating quality-driven tile datasets ranked by nuclei density or cellularity - Performing batch tile extraction across slide collections with consistent parameters - Assessing slide quality and tissue coverage before computational pathology workflows - For raw slide access without tile extraction, use `openslide-python` directly - For complex multiplexed imaging or spatial proteomics pipelines, use `pathml` instead ## Prerequisites - **Python packages**: `histolab` (includes OpenSlide Python bindings) - **System dependency**: OpenSlide C library must be installed separ
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Slide Management
- Module 2: Tissue Detection
- Module 3: Tile Extraction
- Module 4: Filters and Preprocessing
- Module 5: Scoring
- Module 6: Visualization
- Key Concepts
- WSI Pyramid Levels
- Filter Composition Pattern
macOS brew install openslide pip install histolab Ubuntu/Debian sudo apt-get install openslide-tools
What does the histolab-wsi-processing skill do?
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill histolab-wsi-processing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.