wsi-tile-cleanup

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wsi-tile-cleanup

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Image filters for digital pathology: detect pen marks, background, and artifacts. Use them for preprocessing towards deep learning

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# wsi-tile-cleanup

Simple library for detecting gibberish tiles from histopathological whole-slide images (WSI).

By gibberish tiles I mean tiles with pen marks and similar artifacts:

`wsi_tile_cleanup` detects background tiles (based on the [Otsu algorithm](https://en.wikipedia.org/wiki/Otsu%27s_method)), red / green / blue pen marks, and black artifacts.

The typical use case for `wsi_tile_cleanup` is preprocessing whole-slide images (WSI) before loading tiles into a neural network (yes, deep learning).

If you are building a deep learning pipeline, the following repositories might be of interest: [@lucasrla/wsi-preprocessing](https://github.com/lucasrla/wsi-preprocessing) and [@lucasrla/wsi-preprocessing-sos-workflow](https://github.com/lucasrla/wsi-preprocessing-sos-workflow).

## Installation

### Conda

```sh
conda create --name YOUR_ENV_NAME --channel conda-forge python=3.6 libvips pyvips numpy
conda activate YOUR_ENV_NAME

python3.6 -m pip install git+https://github.com/lucasrla/wsi-tile-cleanup.git
# note: `python3.6 -m pip` is to make sure we are using pip from python=3.6
```

### pip or poetry

```sh
# first of all, install libvips 
# https://libvips.github.io/libvips/install.html
# (tip: have it installed with openslide support)

# next, create a new virtualenv and activate it using your tool of choice
# (e.g., pyenv, virtualenv, etc)

# then, depending on your dependency manager, run either:
poetry add git+https://github.com/lucasrla/wsi-tile-cleanup.git
# or
pip install git+https://github.com/lucasrla/wsi-tile-cleanup.git
```

## Usage

```python
from wsi_tile_cleanup import filters, utils

img = utils.read_image("data/images/tiles/5.jpeg")
bands = utils.split_rgb(img)

colors = ["red", "green", "blue"]

for color in colors:
    perc = filters.pen_percentage(bands, color)
    print(f"{color}: {perc:.5f}")
```

See also: [`examples.py`](https://github.com/lucasrla/wsi-tile-cleanup/blob/master/examples.py).

## Credits and Acknowledgments

Please note that `wsi_tile_cleanup` is just a very thin wrapper around `libvips`, `pyvips` and `numpy`. They are the ones doing the heavy lifting (and doing it amazingly well).

- [libvips](https://libvips.github.io/libvips/): A fast image processing library with low memory needs. The official Python bindings are called [pyvips](https://github.com/libvips/pyvips).

- [NumPy](https://numpy.org): The fundamental package for scientific computing with Python.

- [deep-histopath](https://github.com/CODAIT/deep-histopath): I ported [some of their filters](https://github.com/CODAIT/deep-histopath/blob/master/deephistopath/wsi/filter.py) to `pyvips`. If you are interested in a preprocessing pipeline for deep learning, check out their [nice write-up](https://github.com/CODAIT/deep-histopath/blob/master/docs/wsi-preprocessing-in-python/index.md).

- [scikit-image](https://scikit-image.org): I ported their [implementation of the Otsu algorithm](https://github.com/scikit-image/scikit-image/blob/df84166dfac6ade01d496b7593bb8d36a8d6ad68/skimage/filters/thresholding.py#L237) to `pyvips` and `numpy`.

- [Cancer Digital Slide Archive](https://cancer.digitalslidearchive.org): [TCGA](https://en.wikipedia.org/wiki/The_Cancer_Genome_Atlas) slides hosted online by the [Winship Cancer Institute at Emory University](https://winshipcancer.emory.edu).

## License

`wsi-tile-cleanup` is [Free Software](https://www.gnu.org/philosophy/free-sw.html) distributed under the [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/).

Dependencies have their own licenses, check them out.

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