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](https://openslide.org/download/)), then install histolab:

```bash
uv pip install histolab
```

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

```bash
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:

```python
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](references/core_capabilities.md):

1. **Slide management** — opening slides, properties, levels, thumbnails, and scaled images.
2. **Tissue detection and masks** — `TissueMask` and `BiggestTissueBoxMask`, and custom masks.
3. **Tile extraction** — random, grid, and score-based tilers with size, level, and
   tissue-fraction control.
4. **Filters and preprocessing** — image and morphological filters, and composing them.
5. **Stain normalization** — Reinhard and Macenko normalization against a target image.
6. **Visualization** — locating tiles on the slide and inspecting masks and extractions.

Five end-to-end workflows are in
[references/typical_workflows.md](references/typical_workflows.md). Per-topic detail lives
in [references/slide_management.md](references/slide_management.md),
[references/tissue_masks.md](references/tissue_masks.md),
[references/tile_extraction.md](references/tile_extraction.md),
[references/filters_preprocessing.md](references/filters_preprocessing.md), and
[references/visualization.md](references/visualization.md).

## 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 filter