Vahadane stain normalization is being used extensively in Digital Pathology workflows to provide better generalization of Deep Learning models between cohorts.

The StainTools package has been one of the most used and most clear implementation of the Vahadane stain normalization. Unfortunately, StainTools can be slow when used on large images or on a large number of images.

This repository implements a GPU accelerated version of the Vahadane stain normalization using torch.

This repository provides a fully GPU based stain normalization workflow, useful in combination with cuCIM and a faster workflow using CPU based stain matrix estimation with accelerated stain concentration estimation.

For WSI workflows, a fixed target stain matrix can be set, eliminating the need for recalculating the stain matrix for every new image patch and making the transformation fully GPU based.

Benchmarks using LineProfiler show speed increase of TorchVahadane compared to StainTools.

Brackets indicate the transformation speed when using a fixed stain matrix (see robust stain estimation). Measured using python 3.11.3 and spams 2.6.5.4.

The new TorchVahadane version fixes instabilities in the concentration estimation by replacing the ista algorithm with the iterative positive thresholding algorithm (IPTA), which uses the correct positivity constraint for the sparse regularization problem.

TorchVahadane can be employed as a drop-in replacement for StainTools. Per default, the TorchVahadaneNormalizer uses the cuda device and uses staintools based stain_matrix estimation (fastest and most robust approach). As StainTools is now a read-only repository, StainTools is integrated and not used as a dependency. The transform function also has a default parameter to pass-through the generated tissue mask for downstream tasks.

from torchvahadane import TorchVahadaneNormalizer
normalizer = TorchVahadaneNormalizer(device='cuda', staintools_estimate=True)
normalizer.fit(target)
img_normed = normalizer.transform(img)

# return tissue mask
img_normed, img_mask = normalizer.transform(img, return_mask=True)

In practice, Vahadane normalization does not always transfer the saturation and contrast of the reference image to the source image, this retaining a domain shift between the target and source image.

This can be mitigated by using histogram matching (see skimage.exposure.match_histograms).

TorchVahadane implements masked histogram matching using torch, matching the cumulative density function of the histograms only on tissue pixels. This makes histogram matching suitable to work on histology tiles with non-tissue regions.

Histogram matching is integrated into the standard fit/transform pipeline and is enabled by setting correct_exposure to True.

from torchvahadane import TorchVahadaneNormalizer
normalizer = TorchVahadaneNormalizer(correct_exposure=True)
normalizer.fit(target)
normalizer.transform(img)

Masked histogram matching can also be used directly from the histogram_matching module.

TorchVahadane also supports the estimation of median stain intensities, as proposed by Vahadane et al. TorchVahadane samples the WSI over a grid of tiles amd returns the median stain instensities. Openslide is used as an optional dependency to extract the WSI tiles.

Setting a fixed stain_matrix skips the stain_matrix estimation of the source image in the transform, speeding up subsequent transformations.

from torchvahadane.wsi_util import estimate_median_matrix

stain_matrix = estimate_median_matrix(osh, normalizer, osh_level=0, tile_size=4096, num_workers=12)
normalizer.set_stain_matrix(stain_matrix)

TorchVahadane can be installed with pip using

git clone https://github.com/cwlkr/torchvahadane.git
cd torchvahadane
pip install .

or directly

pip install git https://github.com/cwlkr/torchvahadane.git

Spams installation through pip throws more errors than not. Using conda's pre-compiled binaries might work best. Spams is not listed in the package requirements.

Openslide is not listed in the package requirements as it is considered optional.

Several lines of code in this repository are directly adapted from the following repositories I would like to credit with their excelent work!

StainTools
pytorch-lasso
torchstain