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Segmention using DeepLearning ONNX models packaged as *.czann files

Project description


License PyPI Python Version napari hub

Semantic Segmentation using DeepLearning ONNX models packaged as *.czann files.

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

Train on APEER and use model in Napari


  • Please clone this repository first using your favorite tool.

  • Ideally one creates a new conda environment or use an existing environment that already contains Napari.

Feel free to create a new environment using the YAML file at your own risk:

cd the-github-repo-with-YAML-file
conda env create --file conda_env_napari_czann_segment.yml
conda activate napari_czmodel
  • Install the plugin locally

Please run the the following command:

pip install -e .

You can install napari-czann-segment via pip soon, when it will be published:

pip install napari-czann-segment

To install latest development version:

pip install git+

What does the plugin do

The plugin allows you read open a *.czann file contains das Deep Neural Network (ONNX) for semantic segmentation and metadata. Such a model con be created in two ways:

  • Train the segmentation model fully automated on APEER and download the *.czann file
  • Train your model in a Jupyter notebook etc. and package it using the czmodel python package as an *.czann
  • To process also larger multi-dimensional images it uses the cztile package to chunk the individual 2d arrays using a specific overlap.

Using this plugin

Sample Data

A test image and a *.czann model file can be downloaded here.

  • PGC_20X.ome.tiff --> use PGC_20X_nucleus_detector.czann to segment

In order to use this plugin the user has to do the following things:

  • Open the image using "File - Open Files(s)" (requires napari-aicsimageio plugin).
  • Activate the napari-czann-segment plugin from "Plugins".
  • *Select a .czann file to use the model for segmentation.

Napari - Image loaded and czann selected

  • Adjust the minimum overlap used to the tiling (optional).
  • Select the layer to be segmented.
  • Press Segment Selected Image Layer to run the segmentation.

Napari - Image successfully segmented

A successful is obviously only the starting point for further image analysis steps to extract the desired numbers from the segmented image. Another example is shown below demonstrating a simple "Grain Size Analysis" using a deep-learning model trained on APEER used in napari

Napari - Simple Grain Size Analysis


IMPORTANT: Currently the plugin only supports using models trained on a single channel image. Therefore make sure that during the training on APEER or somewhere else the correct inputs images are used. It is quite simple to train an single RGB image, which actually has three channels, load this image in napari and notice only then that the model will not work, because the image will 3 channels inside napari.

  • Only the CPU will be used for the inference using the ONNX runtime for the ONNX-CPU runtime
  • GPUs are not supported yet and will require ONNX-GPU runtime


Contributions and Feedback are very welcome.


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


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

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