Skip to main content

A classification plugin for the ROIs of a segmentation mask.

Project description

# napari_svetlana

License PyPI Python Version tests codecov napari hub Documentation

The aim of this plugin is to classify the output of a segmentation algorithm. The inputs are :

  • A folder of raw images
  • Their segmentation masks where each ROI has its own label.

Svetlana can process 2D, 3D and multichannel image. If you want to use it to work on cell images, we strongly recommend the use of Cellpose for the segmentation part, as it provides excellent quality results and a standard output format accepted by Svetlana (labels masks).

If you use this plugin please cite the paper:

Cazorla, C., Weiss, P., & Morin, R. (2024). Svetlana: a Supervised Segmentation Classifier for Napari.

@article{cazorla2024svetlana,
  title={Svetlana a supervised segmentation classifier for Napari},
  author={Cazorla, Cl{\'e}ment and Morin, Renaud and Weiss, Pierre},
  journal={Scientific Reports},
  volume={14},
  number={1},
  pages={11604},
  year={2024},
  publisher={Nature Publishing Group UK London}
}


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

Installation

First install Napari in a Python 3.9 Conda environment following these instructions :

conda create -n svetlana_env python=3.9
conda activate svetlana_env
conda install pip
python -m pip install "napari[all]"==0.4.17

Then, you can install napari_svetlana via pip, or directly from the Napari plugin manager (see Napari documentation):

pip install napari_svetlana

WARNING:

If you have a Cuda compatible GPU on your computer, some computations may be accelerated using Cupy. Unfortunately, Cupy needs Cudatoolkit to be installed. This library can only be installed via Conda while the plugin is a pip plugin, so it must be installed manually for the moment:

conda install cudatoolkit=10.2 

Also note that the library (Cucim) that we use to improve these performances, computing morphological operations on GPU is unfortunately only available for Linux systems. Hence, if you are a Windows user, this installation is not necessary.

Tutorial

Many advanced features are available in Svetlana, such as data augmentation or contextual information reduction, to optimize the performance of your classifier. Thus, we strongly encourage you to check our Youtube tutorial and our documentation. A button called TRY ON DEMO IMAGE is available in the annotation plugin and enables you to apply the YouTube tutorial to the same test images to learn how to use the plugin. Feel free to try it to test all the features that Svetlana offers.

Similar Napari plugins

Joel Luethi developed a similar method for objects classification called napari feature classifier. Also, apoc by Robert Haase is available in Napari for pixels and objects classification.

Contributing

Contributions are very welcome.

License

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

Acknowledgements

The method was developed by Clément Cazorla, Renaud Morin and Pierre Weiss. And the plugin was written by Clément Cazorla. The project is co-funded by Imactiv-3D and CNRS.

Issues

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

napari_svetlana-1.0.2.tar.gz (95.8 kB view details)

Uploaded Source

Built Distribution

napari_svetlana-1.0.2-py3-none-any.whl (120.3 kB view details)

Uploaded Python 3

File details

Details for the file napari_svetlana-1.0.2.tar.gz.

File metadata

  • Download URL: napari_svetlana-1.0.2.tar.gz
  • Upload date:
  • Size: 95.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.14

File hashes

Hashes for napari_svetlana-1.0.2.tar.gz
Algorithm Hash digest
SHA256 e74b24bf9b7c841ca2d7a153800670a44c3edd24e920fd778dc618aa075d6afc
MD5 227024921f18d9dddba12e8e1dce6f47
BLAKE2b-256 a20b0c1d19bac5f0a4d6fd901fa28e0b55bad4dfb5cb34cf0c57af2b9b3eca72

See more details on using hashes here.

File details

Details for the file napari_svetlana-1.0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for napari_svetlana-1.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 88ae48887223be1f2b834aff3e7324a84171b3524111fc50529db63520c8335b
MD5 fdbc1e72865d90dd6cf21c880add5c6f
BLAKE2b-256 30fbc0423a8cd134121908d63cb51096374b5c1fa5fa5f064eb0ca9aed1508c1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page