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Stain normalization tools for histological analysis and computational pathology

Project description

torchstain

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

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)

alt text

Implemented algorithms

Algorithm numpy torch tensorflow
Macenko

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.

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-rc1},
  month        = aug,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {v1.2.0-rc1},
  doi          = {10.5281/zenodo.6976410},
  url          = {https://doi.org/10.5281/zenodo.6976410}
}

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