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/")
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=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. 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
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}")
slide.save_thumbnail()
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
    slide.save_thumbnail()
    # 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

Always inspect slide properties before processing

Save thumbnails 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 preprocessing filters

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

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.

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