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

uv pip install histolab

Quick Start

Basic workflow for extracting tiles from a whole slide image:

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:

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/")