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

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 to processed_path
from pathlib import Path
Path(slide.processed_path).mkdir(parents=True, exist_ok=True)
slide.thumbnail.save(Path(slide.processed_path) / f"{slide.name}_thumbnail.png")

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=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 (most informative)
score_tiler = ScoreTiler(
    tile_size=(512, 512),
    n_tiles=50,
    scorer=NucleiScorer(),
    level=0
)
score_tiler.extract(slide, report_path="tiles_report.csv")

Always preview before extracting:

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

4. Filters and Preprocessing

Apply image processing filters for tissue detection, quality control, and preprocessing.

Filter categories:

Image Filters: Color space conversions, thresholding, contrast enhancement

• RgbToGrayscale, RgbToHsv, RgbToHed
• OtsuThreshold, AdaptiveThreshold
• StretchContrast, HistogramEqualization

Morphological Filters: Structural operations on binary images

• BinaryDilation, BinaryErosion
• BinaryOpening, BinaryClosing
• RemoveSmallObjects, RemoveSmallHoles

Composition: Chain multiple filters together

• Compose: Create filter pipelines

Reference: references/filters_preprocessing.md contains comprehensive documentation on:

• Detailed explanation of each filter type
• Filter composition and chaining
• Common preprocessing pipelines (tissue detection, pen removal, nuclei enhancement)
• Applying filters to tiles
• Custom mask filters
• Quality control filters (blur detection, tissue coverage)
• Best practices and troubleshooting

Example workflows:

from histolab.filters.compositions import Compose
from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
from histolab.filters.morphological_filters import (
    BinaryDilation, RemoveSmallHoles, RemoveSmallObjects
)

# Standard tissue detection pipeline
tissue_detection = Compose([
    RgbToGrayscale(),
    OtsuThreshold(),
    BinaryDilation(disk_size=5),
    RemoveSmallHoles(area_threshold=1000),
    RemoveSmallObjects(area_threshold=500)
])

# Use with custom mask
from histolab.masks import TissueMask
custom_mask = TissueMask(filters=tissue_detection)

# Apply filters to tile
from histolab.tile import Tile
filtered_tile = tile.apply_filters(tissue_detection)

5. Stain Normalization

Standardize staining appearance across slides for deep learning (added in histolab 0.6.0).

Key classes: MacenkoStainNormalizer, ReinhardStainNormalizer

from histolab.stain_normalizer import MacenkoStainNormalizer, ReinhardStainNormalizer
from PIL import Image

target = Image.open("reference_stain.png")  # Style reference slide/tile
source = Image.open("slide_to_normalize.png")

normalizer = MacenkoStainNormalizer()
normalizer.fit(target)
normalized = normalizer.transform(source)
normalized.save("normalized.png")

Use ReinhardStainNormalizer() for Reinhard color transfer. Fit on a representative target image, then transform source tiles or thumbnails. See references/filters_preprocessing.md for filter-based alternatives.

6. Visualization

Visualize slides, masks, tile locations, and extraction quality.

Common visualization tasks:

• Displaying slide thumbnails
• Visualizing tissue masks
• Previewing tile locations
• Assessing tile quality
• Creating reports and figures

Reference: references/visualization.md contains comprehensive documentation on:

• Slide thumbnail display and saving
• Mask visualization with locate_mask()
• Tile location preview with locate_tiles()
• Displaying extracted tiles and mosaics
• Quality assessment (score distributions, top vs bottom tiles)
• Multi-slide visualization
• Filter effect visualization
• Exporting high-resolution figures and PDF reports
• Interactive visualization in Jupyter notebooks

Example workflows:

import matplotlib.pyplot as plt
from histolab.masks import TissueMask

# Display slide thumbnail
plt.figure(figsize=(10, 10))
plt.imshow(slide.thumbnail)
plt.title(f"Slide: {slide.name}")
plt.axis('off')
plt.show()

# Visualize tissue mask
tissue_mask = TissueMask()
slide.locate_mask(tissue_mask)

# Preview tile locations
tiler = RandomTiler(tile_size=(512, 512), n_tiles=50)
tiler.locate_tiles(slide, n_tiles=20)

# Display extracted tiles in grid
from pathlib import Path
from PIL import Image

tile_paths = list(Path("output/tiles/").glob("*.png"))[:16]
fig, axes = plt.subplots(4, 4, figsize=(12, 12))
axes = axes.ravel()

for idx, tile_path in enumerate(tile_paths):
    tile_img = Image.open(tile_path)
    axes[idx].imshow(tile_img)
    axes[idx].set_title(tile_path.stem, fontsize=8)
    axes[idx].axis('off')

plt.tight_layout()
plt.show()

Typical Workflows

Workflow 1: Exploratory Tile Extraction

Quick sampling of diverse tissue regions for initial analysis.

from histolab.slide import Slide
from histolab.tiler import RandomTiler
from pathlib import Path
import logging

# Enable logging for progress tracking
logging.basicConfig(level=logging.INFO)

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

# Inspect slide
print(f"Dimensions: {slide.dimensions}")
print(f"Levels: {slide.levels}")
Path(slide.processed_path).mkdir(parents=True, exist_ok=True)
slide.thumbnail.save(Path(slide.processed_path) / f"{slide.name}_thumbnail.png")

# Configure random tiler
random_tiler = RandomTiler(
    tile_size=(512, 512),
    n_tiles=100,
    level=0,
    seed=42,
    check_tissue=True,
    tissue_percent=80.0
)

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

# Extract tiles
random_tiler.extract(slide)

Workflow 2: Comprehensive Grid Extraction

Complete tissue coverage for whole-slide analysis.

from histolab.slide import Slide
from histolab.tiler import GridTiler
from histolab.masks import TissueMask

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

# Use TissueMask for all tissue sections
tissue_mask = TissueMask()
slide.locate_mask(tissue_mask)

# Configure grid tiler
grid_tiler = GridTiler(
    tile_size=(512, 512),
    level=1,  # Use level 1 for faster extraction
    pixel_overlap=0,
    check_tissue=True,
    tissue_percent=70.0
)

# Preview grid
grid_tiler.locate_tiles(slide)

# Extract all tiles
grid_tiler.extract(slide, extraction_mask=tissue_mask)

Workflow 3: Quality-Driven Tile Selection

Extract most informative tiles based on nuclei density.

from histolab.slide import Slide
from histolab.tiler import ScoreTiler
from histolab.scorer import NucleiScorer
import pandas as pd
import matplotlib.pyplot as plt

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

# Configure score tiler
score_tiler = ScoreTiler(
    tile_size=(512, 512),
    n_tiles=50,
    level=0,
    scorer=NucleiScorer(),
    check_tissue=True
)

# Preview top tiles
score_tiler.locate_tiles(slide, n_tiles=15)

# Extract with report
score_tiler.extract(slide, report_path="tiles_report.csv")

# Analyze scores
report_df = pd.read_csv("tiles_report.csv")
plt.hist(report_df['score'], bins=20, edgecolor='black')
plt.xlabel('Tile Score')
plt.ylabel('Frequency')
plt.title('Distribution of Tile Scores')
plt.show()

Workflow 4: Multi-Slide Processing Pipeline

Process entire slide collection with consistent parameters.

from pathlib import Path
from histolab.slide import Slide
from histolab.tiler import RandomTiler
import logging

logging.basicConfig(level=logging.INFO)

# Configure tiler once
tiler = RandomTiler(
    tile_size=(512, 512),
    n_tiles=50,
    level=0,
    seed=42,
    check_tissue=True
)

# Process all slides
slide_dir = Path("slides/")
output_base = Path("output/")

for slide_path in slide_dir.glob("*.svs"):
    print(f"\nProcessing: {slide_path.name}")

    # Create slide-specific output directory
    output_dir = output_base / slide_path.stem
    output_dir.mkdir(parents=True, exist_ok=True)

    # Load and process slide
    slide = Slide(slide_path, processed_path=output_dir)

    # Save thumbnail for review
    Path(slide.processed_path).mkdir(parents=True, exist_ok=True)
    slide.thumbnail.save(Path(slide.processed_path) / f"{slide.name}_thumbnail.png")

    # Extract tiles
    tiler.extract(slide)

    print(f"Completed: {slide_path.name}")

Workflow 5: Custom Tissue Detection and Filtering

Handle slides with artifacts, annotations, or unusual staining.

from histolab.slide import Slide
from histolab.masks import TissueMask
from histolab.tiler import RandomTiler
from histolab.filters.compositions import Compose
from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
from histolab.filters.morphological_filters import (
    BinaryDilation, RemoveSmallObjects, RemoveSmallHoles
)

# Define custom filter pipeline for aggressive artifact removal
aggressive_filters = Compose([
    RgbToGrayscale(),
    OtsuThreshold(),
    BinaryDilation(disk_size=10),
    RemoveSmallHoles(area_threshold=5000),
    RemoveSmallObjects(area_threshold=3000)  # Remove larger artifacts
])

# Create custom mask
custom_mask = TissueMask(filters=aggressive_filters)

# Load slide and visualize mask
slide = Slide("slide.svs", processed_path="output/")
slide.locate_mask(custom_mask)

# Extract with custom mask
tiler = RandomTiler(tile_size=(512, 512), n_tiles=100)
tiler.extract(slide, extraction_mask=custom_mask)

Best Practices

Slide Loading and Inspection

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

Tissue Detection

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

Tile Extraction

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

Performance

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

Quality Control

1. Validate tile quality (check for blur, artifacts, focus)
2. Review score distributions for ScoreTiler
3. Inspect top and bottom scoring tiles
4. Monitor tissue coverage statistics
5. 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.