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https://github.com/EIDOSLAB/torchstain
Stain normalization tools for histological analysis and computational pathology
https://github.com/EIDOSLAB/torchstain
computational-pathology digital-pathology histopathology medical-imaging numpy python pytorch stain-normalization tensorflow
Last synced: 3 months ago
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Stain normalization tools for histological analysis and computational pathology
- Host: GitHub
- URL: https://github.com/EIDOSLAB/torchstain
- Owner: EIDOSLAB
- License: mit
- Created: 2020-12-22T10:13:08.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2024-04-01T09:31:08.000Z (7 months ago)
- Last Synced: 2024-05-21T06:14:22.014Z (6 months ago)
- Topics: computational-pathology, digital-pathology, histopathology, medical-imaging, numpy, python, pytorch, stain-normalization, tensorflow
- Language: Python
- Homepage:
- Size: 2.48 MB
- Stars: 102
- Watchers: 5
- Forks: 22
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-pathology - torchstain - Stain normalization transformations. (Software / Model)
README
# torchstain
[](https://opensource.org/licenses/MIT)
[](https://github.com/EIDOSLAB/torchstain/actions)
[](https://pypi.org/project/torchstain/)
[](https://zenodo.org/badge/latestdoi/323590093)
GPU-accelerated stain normalization tools for histopathological images. Compatible with PyTorch, TensorFlow, and Numpy.
Normalization algorithms currently implemented:
- Macenko [\[1\]](#reference) (ported from [numpy implementation](https://github.com/schaugf/HEnorm_python))
- Reinhard [\[2\]](#reference)
- Modified Reinhard [\[3\]](#reference)
## Installation
```bash
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
```python
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:
```bibtex
@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](https://github.com/EIDOSLAB/UNITOPATHO) data collection, which you can cite as:
```bibtex
@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}
}
```