Histolab

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

Install OpenSlide system libraries first (OpenSlide download), then install histolab:

uv pip install histolab

For built-in TCGA sample slides via histolab.data, also install pooch:

uv pip install pooch

Histolab 0.7.0 (latest stable) supports Python 3.8–3.11 on Linux and macOS. Windows is not supported as of 0.7.0.

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

Six capability areas, each with worked code, are documented in

references/core_capabilities.md:

• Slide management — opening slides, properties, levels, thumbnails, and scaled images.
• Tissue detection and masks — TissueMask and BiggestTissueBoxMask, and custom masks.
• Tile extraction — random, grid, and score-based tilers with size, level, and

tissue-fraction control.

• Filters and preprocessing — image and morphological filters, and composing them.
• Stain normalization — Reinhard and Macenko normalization against a target image.
• Visualization — locating tiles on the slide and inspecting masks and extractions.

Five end-to-end workflows are in

references/typical_workflows.md. Per-topic detail lives

in references/slide_management.md,

references/tissue_masks.md,

references/tile_extraction.md,

references/filters_preprocessing.md, and

references/visualization.md.

Best Practices

Slide Loading and Inspection

• Always inspect slide properties before processing
• Save thumbnails with slide.thumbnail.save() for quick visual review
• Check pyramid levels and dimensions
• Verify tissue is present using thumbnails

Tissue Detection

• Preview masks with locate_mask() before extraction
• Use TissueMask for multiple sections, BiggestTissueBoxMask for single sections
• Customize filters for specific stains (H&E vs IHC)
• Handle pen annotations with custom masks
• Test masks on diverse slides

Tile Extraction

• Always preview with locate_tiles() before extracting
• Choose appropriate tiler:
• RandomTiler: Sampling and exploration
• GridTiler: Complete coverage
• ScoreTiler: Quality-driven selection
• Set appropriate tissue_percent threshold (70-90% typical)
• Use seeds for reproducibility in RandomTiler
• Extract at appropriate pyramid level for analysis resolution
• Enable logging for large datasets

Performance

• Extract at lower levels (1, 2) for faster processing
• Use BiggestTissueBoxMask over TissueMask when appropriate
• Adjust tissue_percent to reduce invalid tile attempts
• Limit n_tiles for initial exploration
• Use pixel_overlap=0 for non-overlapping grids

Quality Control

• Validate tile quality (check for blur, artifacts, focus)
• Review score distributions for ScoreTiler
• Inspect top and bottom scoring tiles
• Monitor tissue coverage statistics
• Filter extracted tiles by additional quality metrics if needed

Common Use Cases

Training Deep Learning Models

• Extract balanced datasets using RandomTiler across multiple slides
• Use ScoreTiler with NucleiScorer to focus on cell-rich regions
• Extract at consistent resolution (level 0 or level 1)
• Generate CSV reports for tracking tile metadata

Whole Slide Analysis

• Use GridTiler for complete tissue coverage
• Extract at multiple pyramid levels for hierarchical analysis
• Maintain spatial relationships with grid positions
• Use pixel_overlap for sliding window approaches

Tissue Characterization

• Sample diverse regions with RandomTiler
• Quantify tissue coverage with masks
• Extract stain-specific information with HED decomposition
• Compare tissue patterns across slides

Quality Assessment

• Identify optimal focus regions with ScoreTiler
• Detect artifacts using custom masks and filters
• Assess staining quality across slide collection
• Flag problematic slides for manual review

Dataset Curation

• Use ScoreTiler to prioritize informative tiles
• Filter tiles by tissue percentage
• Generate reports with tile scores and metadata
• Create stratified datasets across slides and tissue types

Troubleshooting

No tiles extracted

• Lower tissue_percent threshold
• Verify slide contains tissue (check thumbnail)
• Ensure extraction_mask captures tissue regions
• Check tile_size is appropriate for slide resolution

Many background tiles

• Enable check_tissue=True
• Increase tissue_percent threshold
• Use appropriate mask (TissueMask vs BiggestTissueBoxMask)
• Customize mask filters to better detect tissue

Extraction very slow

• Extract at lower pyramid level (level=1 or 2)
• Reduce n_tiles for RandomTiler/ScoreTiler
• Use RandomTiler instead of GridTiler for sampling
• Use BiggestTissueBoxMask instead of TissueMask

Tiles have artifacts

• Implement custom annotation-exclusion masks
• Adjust filter parameters for artifact removal
• Increase small object removal threshold
• Apply post-extraction quality filtering

Inconsistent results across slides

• Use same seed for RandomTiler
• Normalize staining with MacenkoStainNormalizer or ReinhardStainNormalizer
• Adjust tissue_percent per staining quality
• Implement slide-specific mask customization

Resources

This skill includes detailed reference documentation in the references/ directory:

references/slide_management.md

Comprehensive guide to loading, inspecting, and working with whole slide images:

• Slide initialization and configuration
• Built-in sample datasets
• Slide properties and metadata
• Thumbnail generation and visualization
• Working with pyramid levels
• Multi-slide processing workflows
• Best practices and common patterns

references/tissue_masks.md

Complete documentation on tissue detection and masking:

• TissueMask, BiggestTissueBoxMask, BinaryMask classes
• How tissue detection filters work
• Customizing masks with filter chains
• Visualizing masks
• Creating custom rectangular and annotation-exclusion masks
• Integration with tile extraction
• Best practices and troubleshooting

references/tile_extraction.md

Detailed explanation of tile extraction strategies:

• RandomTiler, GridTiler, ScoreTiler comparison
• Available scorers (NucleiScorer, CellularityScorer, custom)
• Common and strategy-specific parameters
• Tile preview with locate_tiles()
• Extraction workflows and CSV reporting
• Advanced patterns (multi-level, hierarchical)
• Performance optimization
• Troubleshooting common issues

references/filters_preprocessing.md

Complete filter reference and preprocessing guide:

• Image filters (color conversion, thresholding, contrast)
• Morphological filters (dilation, erosion, opening, closing)
• Filter composition and chaining
• Built-in stain normalization (Macenko, Reinhard) and filter-based alternatives
• Common preprocessing pipelines
• Applying filters to tiles
• Custom mask filters
• Quality control filters
• Best practices and troubleshooting

references/visualization.md

Comprehensive visualization guide:

• Slide thumbnail display and saving
• Mask visualization techniques
• Tile location preview
• Displaying extracted tiles and creating mosaics
• Quality assessment visualizations
• Multi-slide comparison
• Filter effect visualization
• Exporting high-resolution figures and PDFs
• Interactive visualization in Jupyter notebooks

Usage pattern: Reference files contain in-depth information to support workflows described in this main skill document. Load specific reference files as needed for detailed implementation guidance, troubleshooting, or advanced features.