torchstain 1.1.0

Pytorch stain normalization utils

torchstain

Pytorch-compatible normalization tools for histopathological images. Normalization algorithms currently implemented:

• Macenko et al. [1] (ported from numpy implementation)

Installation

pip3 install torchstain

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.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)

Backend comparison

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

size	numpy avg. time	numpy tot. time	torch avg. time	torch tot. time
224x224	0.0323s ± 0.0032	0.3231s	0.0234s ± 0.0384	0.2340s
448x448	0.1228s ± 0.0042	1.2280s	0.0395s ± 0.0168	0.3954s
672x672	0.2653s ± 0.0106	2.6534s	0.0753s ± 0.0157	0.7527s
896x896	0.4940s ± 0.0208	4.9397s	0.1262s ± 0.0159	1.2622s
1120x1120	0.6888s ± 0.0081	6.8883s	0.2002s ± 0.0141	2.0021s
1344x1344	1.0145s ± 0.0089	10.1448s	0.2703s ± 0.0136	2.7026s
1568x1568	1.2620s ± 0.0133	12.6200s	0.3680s ± 0.0128	3.6795s
1792x1792	1.4289s ± 0.0128	14.2886s	0.5968s ± 0.0160	5.9676s

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.