stainx 0.0.19

Enhanced stain normalization for histopathology images with batch processing support. Optimized for CPU, GPU (CUDA), and MPS (Apple Silicon) devices.

StainX

Enhanced stain normalization for histopathology images with batch processing support. Optimized for CPU, GPU (CUDA), and MPS (Apple Silicon) devices.

Features

• Multiple algorithms: Histogram Matching, Reinhard, and Macenko normalization
• Automatic backend selection: PyTorch (CPU/GPU/MPS) or optimized CUDA kernels
• Batch processing: Enhanced normalization through efficient batch processing of multiple images
• Flexible device support: CPU, CUDA, MPS (Apple Silicon)

Installation

Requirements

• Python >=3.11
• PyTorch >=2.0.0
• CUDA (optional, for GPU acceleration)

Install from PyPI

pip install stainx

Install from source

git clone https://github.com/rendeirolab/stainx.git
cd stainx
pip install .

CUDA extensions will be automatically built if CUDA is available.

Quick Start

import torch
from stainx import Reinhard, Macenko, HistogramMatching

# Load your images as torch.Tensor (N, C, H, W) or (N, H, W, C)
reference_image = torch.randn(1, 3, 512, 512)  # Reference image
source_images = torch.randn(10, 3, 512, 512)  # Images to normalize

# Reinhard normalization
normalizer = Reinhard(device="cuda")  # or "cpu"
normalizer.fit(reference_image)
normalized = normalizer.transform(source_images)

# Macenko normalization
normalizer = Macenko(device="cuda")
normalizer.fit(reference_image)
normalized = normalizer.transform(source_images)

# Histogram Matching
normalizer = HistogramMatching(device="cuda", channel_axis=1)
normalizer.fit(reference_image)
normalized = normalizer.transform(source_images)

API

All normalizers follow a scikit-learn-like interface:

• fit(reference_images): Compute normalization parameters from reference image(s)
• transform(images): Apply normalization to images
• fit_transform(images): Fit and transform in one step

Available Normalizers

• Reinhard: Reinhard color normalization
• Macenko: Macenko stain separation and normalization
• HistogramMatching: Histogram matching normalization

Backend Selection

Backends are automatically selected based on device availability:

• CUDA: Used when CUDA is available and device is set to CUDA
• PyTorch: Fallback backend, works on CPU and GPU

You can explicitly specify a backend:

normalizer = Reinhard(device="cuda", backend="torch")  # Force Torch backend

Requirements

• Python >=3.11
• PyTorch >=2.0.0
• CUDA Toolkit (optional, for CUDA backend)

License

This project is licensed under the GNU General Public License v3 (GPL-3.0-or-later).

Contributing

See CONTRIBUTING.md for guidelines on contributing to StainX.

Links

• GitHub: https://github.com/rendeirolab/stainx
• Issues: https://github.com/rendeirolab/stainx/issues