iacs-ipac-reader

A plugin used a convolutional neural network (CNN) to distinguish single platelets, platelet clusters, and white blood cells and performed classical image analysis for each subpopulation individually. Based on the derived single-cell features for each population, a Random Forest (RF) model was trained and used to classify COVID-19 associated thrombosis and non-COVID-19 associated thrombosis.

More information about IACS/iPAC.
IACS: DOI: 10.1016/j.cell.2018.08.028
iPAC: DOI: 10.7554/eLife.52938

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This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation¶

You can install iacs_ipac_reader via pip:

pip install iacs_ipac_reader

To install latest development version :

pip install git+https://github.com/zcqwh/iacs_ipac_reader.git

Introduction¶

The iacs-ipac-reader plugin mainly include 3 functional tabs:

• iPAC
• IACS
• AID classif.

iPAC image contour tracker¶

Interface of iPAC contour tracker

IACS image contour tracker¶

Interface of IACS contour tracker

AID classif.¶

Interface of AID classif.

Contributing¶

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License¶

Distributed under the terms of the BSD-3 license, "iacs_ipac_reader" is free and open source software

Issues¶

If you encounter any problems, please file an issue along with a detailed description.