HistolabSAFE

The largest open-source medical AI skills library for OpenClaw🦞.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: histolab
description: Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.
---

# 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.

## Installation

```bash
uv pip install histolab
```

## Quick Start

Basic workflow for extracting tiles from a whole slide image:

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

# Load slide
slide = Slide("slide.svs", processed_path="output/")

# Configure tiler
tiler = RandomTiler(
    tile_size=(512, 512),
    n_tiles=100,
    level=0,
    seed=42
)

# Preview tile locations
tiler.locate_tiles(slide, n_tiles=20)

# Extract tiles
tiler.extract(slide)
```

## Core Capabilities

### 1. Slide Management

Load, inspect, and work with whole slide images in various formats.

**Common operations:**
- Loading WSI files (SVS, TIFF, NDPI, etc.)
- Accessing slide metadata (dimensions, magnification, properties)
- Generating thumbnails for visualization
- Working with pyramidal image structures
- Extracting regions at specific coordinates

**Key classes:** `Slide`

**Reference:** `references/slide_management.md` contains comprehensive documentation on:
- Slide initialization and configuration
- Built-in sample datasets (prostate, ovarian, breast, heart, kidney tissues)
- Accessing slide properties and metadata
- Thumbnail generation and visualization
- Working with pyramid levels
- Multi-slide processing workflows

**Example workflow:**
```python
from histolab.slide import Slide
from histolab.data import prostate_tissue

# Load sample data
prostate_svs, prostate_path = prostate_tissue()

# Initialize slide
slide = Slide(prostate_path, processed_path="output/")

# Inspect properties
print(f"Dimensions: {slide.dimensions}")
print(f"Levels: {slide.levels}")
print(f"Magnification: {slide.properties.get('openslide.objective-power')}")

# Save thumbnail
slide.save_thumbnail()
```

### 2. Tissue Detection and Masks

Automatically identify tissue regions and filter background/artifacts.

**Common operations:**
- Creating binary tissue masks
- Detecting largest tissue region
- Excluding background and artifacts
- Custom tissue segmentation
- Removing pen annotations

**Key classes:** `TissueMask`, `BiggestTissueBoxMask`, `BinaryMask`

**Reference:** `references/tissue_masks.md` contains comprehensive documentation on:
- TissueMask: Segments all tissue regions using automated filters
- BiggestTissueBoxMask: Returns bounding box of largest tissue region (default)
- BinaryMask: Base class for custom mask implementations
- Visualizing masks with `locate_mask()`
- Creating custom rectangular and annotation-exclusion masks
- Mask integration with tile extraction
- Best practices and troubleshooting

**Example workflow:**
```python
from histolab.masks import TissueMask, BiggestTissueBoxMask

# Create tissue mask for all tissue regions
tissue_mask = TissueMask()

# Visualize mask on slide
slide.locate_mask(tissue_mask)

# Get mask array
mask_array = tissue_mask(slide)

# Use largest tissue region (default for most extractors)
biggest_mask = BiggestTissueBoxMask()
```

**When to use each mask:**
- `TissueMask`: Multiple tissue sections, comprehensive analysis
- `BiggestTissueBoxMask`: Single main tissue section, exclude artifacts (default)
- Custom `BinaryMask`: Specific ROI, exclude annotations, custom segmentation

### 3. Tile Extraction

Extract smaller regions from large WSI using different strategies.

**Three extraction strategies:**

**RandomTiler:** Extract fixed number of randomly positioned tiles
- Best for: Sampling diverse regions, exploratory analysis, training data
- Key parameters: `n_tiles`, `seed` for reproducibility

**GridTiler:** Systematically extract tiles across tissue in grid pattern
- Best for: Complete coverage, spatial analysis, reconstruction
- Key parameters: `pixel_overlap` for sliding windows

**ScoreTiler:** Extract top-ranked tiles based on scoring functions
- Best for: Most informative regions, quality-driven selection
- Key parameters: `scorer` (NucleiScorer, CellularityScorer, custom)

**Common parameters:**
- `tile_size`: Tile dimensions (e.g., (512, 512))
- `level`: Pyramid level for extraction (0 = highest resolution)
- `check_tissue`: Filter tiles by tissue content
- `tissue_percent`: Minimum tissue coverage (default 80%)
- `extraction_mask`: Mask defining extraction region

**Reference:** `references/tile_extraction.md` contains comprehensive documentation on:
- Detailed explanation of each tiler strategy
- Available scorers (NucleiScorer, CellularityScorer, custom)
- Tile preview with `locate_tiles()`
- Extraction workflows and reporting
- Advanced patterns (multi-level, hierarchical extraction)
- Performance optimization and troubleshooting

**Example workflows:**

```python
from histolab.tiler import RandomTiler, GridTiler, ScoreTiler
from histolab.scorer import NucleiScorer

# Random sampling (fast, diverse)
random_tiler = RandomTiler(
    tile_size=(512, 512),
    n_tiles=100,
    level=0,
    seed=42,
    check_tissue=True,
    tissue_percent=80.0
)
random_tiler.extract(slide)

# Grid coverage (comprehensive)
grid_tiler = GridTiler(
    tile_size=(512, 512),
    level=0,
    pixel_overlap=0,
    check_tissue=True
)
grid_tiler.extract(slide)

# Score-based selection

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