{"name":"iacs-ipac-reader","display_name":"iacs-ipac-reader","visibility":"public","icon":"","categories":[],"schema_version":"0.2.0","on_activate":null,"on_deactivate":null,"contributions":{"commands":[{"id":"iacs-ipac-reader.iacs_ipac_reader","title":"iacs_ipac_reader","python_name":"iacs_ipac_reader._dock_widget:iacs_ipac_reader","short_title":null,"category":null,"icon":null,"enablement":null}],"readers":null,"writers":null,"widgets":[{"command":"iacs-ipac-reader.iacs_ipac_reader","display_name":"iacs_ipac_reader","autogenerate":false}],"sample_data":null,"themes":null,"menus":{},"submenus":null,"keybindings":null,"configuration":[]},"package_metadata":{"metadata_version":"2.1","name":"iacs-ipac-reader","version":"0.0.13","dynamic":null,"platform":["UNKNOWN"],"supported_platform":null,"summary":"A reader plugin for read iacs/ipac images and export .rtdc files.","description":"# iacs_ipac_reader\n\n[![License](https://img.shields.io/pypi/l/iacs_ipac_reader.svg?color=green)](https://github.com/zcqwh/iacs_ipac_reader/raw/main/LICENSE)\n[![PyPI](https://img.shields.io/pypi/v/iacs_ipac_reader.svg?color=green)](https://pypi.org/project/iacs_ipac_reader)\n[![Python Version](https://img.shields.io/pypi/pyversions/iacs_ipac_reader.svg?color=green)](https://python.org)\n[![tests](https://github.com/zcqwh/iacs_ipac_reader/workflows/tests/badge.svg)](https://github.com/zcqwh/iacs_ipac_reader/actions)\n[![codecov](https://codecov.io/gh/zcqwh/iacs_ipac_reader/branch/main/graph/badge.svg)](https://codecov.io/gh/zcqwh/iacs_ipac_reader)\n[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/iacs_ipac_reader)](https://napari-hub.org/plugins/iacs_ipac_reader)\n\nA 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.\n\nMore information about IACS/iPAC. \n__IACS__: DOI: [10.1016/j.cell.2018.08.028](https://www.sciencedirect.com/science/article/pii/S0092867418310444) \n__iPAC__: DOI: [10.7554/eLife.52938](https://elifesciences.org/articles/52938)\n\n----------------------------------\n\nThis [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.\n\n\n\n## Installation\n\nYou can install `iacs_ipac_reader` via [pip]:\n\n pip install iacs_ipac_reader\n\n\n\nTo install latest development version :\n\n pip install git+https://github.com/zcqwh/iacs_ipac_reader.git\n\n\n## Introduction\n\nThe iacs-ipac-reader plugin mainly include 3 functional tabs:\n\n* iPAC\n* IACS\n* AID classif.\n\n\n\n### iPAC image contour tracker\n