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README.rst

README.rst

Determination of NET Content

This package contains support material (code & data) for the paper:

Automatic Determination of NET (Neutrophil Extracellular Traps) Coverage in Fluorescent Microscopy Images by Luis Pedro Coelho, Catarina Pato, Ana Friães, Ariane Neumann, Maren von Köckritz-Blickwede, Mário Ramirez, and João André Carriço. Currently under review.

Upon publication, all the code will be made available under the MIT license. Use in academic publications should cite the paper above.

Data

Original data is available in the directory data/. The naming structure is as follows:

  • prefix "image"
  • nr of the sample
  • nr of the field inside the sample
  • channel (protein, dna, rois, or rois2).

For example, the file image_25_00_protein.png is from image 25, index 0, and is the protein channel. The ROI files are the result of human labeling.

See the manuscript for details on data acquisition.

Source code

The source code is split into two directories

  • nets this is the library code, which is useful to adapt to new projects.
  • reproduce in this directory, you will find all the necessary code to

reproduce all figures in the paper (including supplemental material).

jugfile.py
This is the central file which runs the whole analysis
compare.py
This script performs the reported comparison between the two operators
bernsen_thresholding.py
This script evaluates Bernsen thresholding for different sets of parameters
compare-example.py
This builds a side-by-side Figure showing differences between operators.
draw-composites.py
This draws composite images for all inputs images

Adapting to your own data

There is a detailed tutorial on how to use the library.

Dependencies

For running on your own data:

  • numpy
  • scikit-learn
  • mahotas

Additionally, for reproducing our experiments:

  • jug
  • pandas
  • matplotlib
  • seaborn

The file requirements.txt in the source-code directory lists all the requirements. If you have permission to do so, running the following command inside that directory should install all dependencies:

pip install -r requirements.txt

If this fails, try:

sudo pip install -r requirements.txt

Reproducing the paper

The results of the paper can be reproduced on a Unix-like machine by running the reproduce.sh script inside source-code/reproduce after having installed the the requirements as listed above.

To use multiple processors, edit this script and set the value of the NR_CPUS variable.