torchstain

GPU-accelerated stain normalization tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy. Normalization algorithms currently implemented:

• Macenko et al. [1] (ported from numpy implementation)
• Reinhard et al. [2] (only numpy & TensorFlow backend support)

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)
])

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

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

Implemented algorithms

Backend comparison

Results with 10 runs per size on a Intel(R) Core(TM) i5-8365U CPU @ 1.60GHz

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.

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}
}