wsi-toolbox 0.3.0

A comprehensive toolkit for Whole Slide Image processing, feature extraction, and clustering analysis

WSI Toolbox

Note: This package is currently unstable. API may change without notice.

A comprehensive toolkit for Whole Slide Image (WSI) processing, feature extraction, and clustering analysis.

Installation

# From PyPI
pip install wsi-toolbox

# From GitHub (latest)
pip install git+https://github.com/technoplasm/wsi-toolbox.git

Quick Start

As a Python Library

import wsi_toolbox as wt

wt.set_default_model_preset('uni')
cmd = wt.Wsi2HDF5Command(patch_size=256)
result = cmd('input.ndpi', 'output.h5')

See README_API.md for API documentation.

As a CLI Tool

After pip install wsi-toolbox, the CLI is available as wsi-toolbox or wt. For development, use uv run wt.

# Extract tile patches from WSI into HDF5
wt wsi2h5 -i input.ndpi -o output.h5

# Extract features using foundation model
wt extract -i output.h5

# Run Leiden clustering on embeddings
wt cluster -i output.h5

# Compute UMAP projection
wt umap -i output.h5

# Compute PCA projection
wt pca -i output.h5

# Generate cluster overlay preview image
wt preview -i output.h5

# Generate PCA score heatmap preview
wt preview-score -i output.h5 -n pca1

# Show HDF5 file structure
wt show -i output.h5

# Export WSI to Deep Zoom Image format
wt dzi -i input.ndpi -o ./output

# Generate thumbnail from WSI
wt thumb -i input.ndpi

Each subcommand has detailed help: wt <subcommand> --help

Streamlit Web Application

uv run task app

HDF5 File Structure

WSI-toolbox stores all data in a single HDF5 file.

Core Data

patches                    # Patch images: [N, H, W, 3]
coordinates                # Patch pixel coordinates: [N, 2]

Metadata

Metadata is stored in file attrs (recommended):

with h5py.File('output.h5', 'r') as f:
    mpp = f.attrs['mpp']
    patch_size = f.attrs['patch_size']
    patch_count = f.attrs['patch_count']
    # ...

Available attrs: original_mpp, original_width, original_height, image_level, mpp, scale, patch_size, patch_count, cols, rows

Legacy metadata/* datasets are kept for backward compatibility but attrs are preferred.

Model Features

{model}/features           # Patch features: [N, D]
                           #   uni: [N, 1024]
                           #   gigapath: [N, 1536]
                           #   virchow2: [N, 2560]
{model}/latent_features    # Latent features (optional): [N, L, D]

Clustering & Analysis (Hierarchical)

Results are stored in a hierarchical namespace structure:

{model}/{namespace}/clusters     # Cluster labels: [N]
{model}/{namespace}/umap         # UMAP coordinates: [N, 2]
{model}/{namespace}/pca1         # PCA scores: [N] or [N, k]

Namespace:

• Single file: default
• Multiple files: file1+file2+... (auto-generated from filenames)

Filter hierarchy: Sub-clustering creates nested paths:

# Base clustering
uni/default/clusters

# Sub-cluster patches in clusters 1, 2, 3
uni/default/filter/1+2+3/clusters

# Further sub-cluster within that
uni/default/filter/1+2+3/filter/0+1/clusters

Each level stores its own clusters, umap, pca results independently.

Dataset Writing Status

Large datasets (patches, features, latent_features) have a writing attribute to indicate write status (True during write, False when complete). Incomplete datasets are automatically deleted on error.

ds = f['patches']  # or f['uni/features']
if ds.attrs.get('writing', False):
    raise RuntimeError('Dataset is incomplete')

Features

• WSI processing (.ndpi, .svs, .tiff → HDF5)
• Feature extraction (UNI, Gigapath, Virchow2)
• Leiden clustering with UMAP visualization
• Preview generation (cluster overlays, PCA heatmaps)
• Type-safe command pattern with Pydantic results
• CLI, Python API, and Streamlit GUI

Documentation

• API Guide - Python API documentation

Development

# Clone and install
git clone https://github.com/technoplasm/wsi-toolbox.git
cd wsi-toolbox
uv sync

# Run CLI
uv run wt --help

# Run Streamlit app
uv run task app

Optional: Gigapath support

uv sync --group gigapath

License

MIT