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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
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
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
The iacs-ipac-reader plugin mainly include 3 functional tabs:
- iPAC
- IACS
- AID classif.
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.
Distributed under the terms of the BSD-3 license, "iacs_ipac_reader" is free and open source software
If you encounter any problems, please file an issue along with a detailed description.