/histolab

name: histolab
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.
license: Apache-2.0 license
metadata:
    skill-author: K-Dense Inc.

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.

Routing Boundary

Use this skill for basic WSI tile extraction, tissue detection, H&E tile preprocessing, and quick histolab dataset preparation. Full computational pathology workflows, PathML pipelines, nucleus segmentation, spatial pathology, multiplex pathology, DICOM/IDC retrieval, OMERO server work, and generic image-processing tasks are outside this skill.

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/")

# 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:**

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:**

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=1