histoprep 1.0.4

Preprocessing module for large histological images.

HistoPrep

Preprocessing large medical images for machine learning made easy!

Description • Installation • Documentation • How To Use • Examples • What's coming? • Citation

Description

This module allows you to easily cut and preprocess large histological slides.

• Cut tiles from large slide images.
• Dearray TMA spots (and cut tiles from individual spots).
• Preprocess extracted tiles automatically.

Installation

pip install histoprep

Cutting slide into tiles

HistoPrep can be used easily to prepare histological slide images for machine learning tasks.

You can either use HistoPrep as a python module...

import histoprep

# Cutting tiles is super easy!
reader = histoprep.SlideReader('/path/to/slide')
metadata = reader.save_tiles(
    '/path/to/output_folder',
    coordinates=reader.get_tile_coordinates(
        width=512, 
        overlap=0.1, 
        max_background=0.96
    ),
)

or as an excecutable from your command line!

jopo666@MacBookM1$ HistoPrep input_dir output_dir width {optional arguments}

Preprocessing

After the tiles have been saved, preprocessing is just a simple outlier detection from the preprocessing metrics saved in tile_metadata.csv!

from histoprep import OutlierDetector
from histoprep.helpers import combine metadata

# Let's combine all metadata from the cut slides
metadata = collect_metadata("/path/to/output_folder", "tile_metadata.csv")
metadata["outlier"] = False 
# Then mark any outlying values!
metadata.loc[metadata['sharpness_max'] < 5, "outlier"] = True     # blurry
metadata.loc[metadata['black_pixels'] > 0.05, "outlier"] = True   # data loss
metadata.loc[metadata['saturation_mean'] > 230, "outlier"] = True # weird blue shit

# This can also be done automatically!
detector = OutlierDetector(metadata, num_clusters=10)
# Plot clusters from most likely outlier to least likely outlier
detector.plot_clusters()
# After visual inspection we can discard some clusters as outliers.
metadata.loc[detector.clusters < 2, "outlier"] = True 

Examples

Examples can be found in the docs.

What's coming?

HistoPrep is under constant development. If there are some features you would like to be added, just submit an issue and we'll start working on the feature!

Citation

If you use HistoPrep in a publication, please cite the github repository.

@misc{histoprep2021,
  author = {Pohjonen J. and Ariotta. V},
  title = {HistoPrep: Preprocessing large medical images for machine learning made easy!},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/jopo666/HistoPrep}},
}