torchstain

GPU-accelerated stain tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy.

Normalization algorithms currently implemented:

• Macenko [1] (ported from numpy implementation)
• Reinhard [2]
• Modified Reinhard [3]
• Multi-target Macenko [4]

Augmentation algorithms currently implemented:

• Macenko-Aug [1] (inspired by StainTools)

Installation

pip install torchstain

To install a specific backend use either torchstain[torch] or torchstain[tf]. The numpy backend is included by default in both.

Example Usage

import torch
from torchvision import transforms
import torchstain
import cv2

target = cv2.cvtColor(cv2.imread("./data/target.png"), cv2.COLOR_BGR2RGB)
to_transform = cv2.cvtColor(cv2.imread("./data/source.png"), cv2.COLOR_BGR2RGB)

T = transforms.Compose([
    transforms.ToTensor(),
    transforms.Lambda(lambda x: x*255)
])

normalizer = torchstain.normalizers.MacenkoNormalizer(backend='torch')
normalizer.fit(T(target))

t_to_transform = T(to_transform)
norm, H, E = normalizer.normalize(I=t_to_transform, stains=True)

Implemented algorithms

Algorithm	numpy	torch	tensorflow
Macenko	✓	✓	✓
Reinhard	✓	✓	✓
Modified Reinhard	✓	✓	✓
Multi-target Macenko	✗	✓	✗
Macenko-Aug	✓	✓	✓

Backend comparison

Runtimes using the Macenko algorithm using different backends. Metrics were calculated from 10 repeated runs for each quadratic image size on an Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz.

size	numpy avg. time	torch avg. time	tf avg. time
224	0.0182s ± 0.0016	0.0180s ± 0.0390	0.0048s ± 0.0002
448	0.0880s ± 0.0224	0.0283s ± 0.0172	0.0210s ± 0.0025
672	0.1810s ± 0.0139	0.0463s ± 0.0301	0.0354s ± 0.0018
896	0.3013s ± 0.0377	0.0820s ± 0.0329	0.0713s ± 0.0008
1120	0.4694s ± 0.0350	0.1321s ± 0.0237	0.1036s ± 0.0042
1344	0.6640s ± 0.0553	0.1665s ± 0.0026	0.1663s ± 0.0021
1568	1.1935s ± 0.0739	0.2590s ± 0.0088	0.2531s ± 0.0031
1792	1.4523s ± 0.0207	0.3402s ± 0.0114	0.3080s ± 0.0188

Reference

• [1] Macenko, Marc et al. "A method for normalizing histology slides for quantitative analysis." 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2009.
• [2] Reinhard, Erik et al. "Color transfer between images." IEEE Computer Graphics and Applications. IEEE, 2001.
• [3] Roy, Santanu et al. "Modified Reinhard Algorithm for Color Normalization of Colorectal Cancer Histopathology Images". 2021 29th European Signal Processing Conference (EUSIPCO), IEEE, 2021.
• [4] Ivanov, Desislav et al. "Multi-target stain normalization for histology slides". arXiv (preprint). 2024.

Citing

If you find this software useful for your research, please cite it as:

@software{barbano2022torchstain,
  author       = {Carlo Alberto Barbano and André Pedersen},
  title        = {EIDOSLAB/torchstain: v1.2.0-stable},
  month        = aug,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v1.2.0-stable},
  doi          = {10.5281/zenodo.6979540},
  url          = {https://doi.org/10.5281/zenodo.6979540}
}

Torchstain was originally developed within the UNITOPATHO data collection, which you can cite as:

@inproceedings{barbano2021unitopatho,
  title={UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading},
  author={Barbano, Carlo Alberto and Perlo, Daniele and Tartaglione, Enzo and Fiandrotti, Attilio and Bertero, Luca and Cassoni, Paola and Grangetto, Marco},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
  pages={76--80},
  year={2021},
  organization={IEEE}
}