# Histolab

Lightweight WSI tile extraction and preprocessing.

- Repository: https://github.com/synthetic-sciences/openscience (4321 stars)
- Category: Documents
- License: Apache-2.0 license
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add synthetic-sciences/openscience --skill histolab -g -a claude-code -y`
Codex: `npx skills add synthetic-sciences/openscience --skill histolab -g -a codex -y`
Cursor: `npx skills add synthetic-sciences/openscience --skill histolab -g -a cursor -y`
Gemini CLI: `npx skills add synthetic-sciences/openscience --skill histolab -g -a gemini-cli -y`

## Description

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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/")

The rest: https://github.com/synthetic-sciences/openscience/blob/HEAD/backend/cli/skills/biology/histolab/SKILL.md