torchstain

GPU-accelerated stain normalization 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]

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	✓	✓	✓

Backend comparison

Results with 10 runs per size on a 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.

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}
}

nuocheng / torchstain