CZANN Segmentation


Semantic Segmentation using Deep Learning ONNX models packaged as *.czann files

License PyPI Python Version napari hub

Semantic Segmentation of multidimensional images using Deep Learning 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


Before installing, please setup a conda environment. If you have never worked with conda environments, go through this tutorial first.

You can then install napari-czann-segment via pip:

pip install napari-czann-segment

What does the plugin do

The plugin allows you to:

  • Use a *.czann file containing the Deep Neural Network (ONNX) for semantic segmentation and metadata
  • Segmentation will be applied per 2D plane for all dimensions
  • Processing larger multidimensional images it uses the cztile package to chunk the individual 2d arrays using a specific overlap.
  • multidimensional images will be processed plane-by-plane

What does the plugin NOT do

Before one can actually use a model it needs to be trained, which is NOT done by this plugin.

There are two main ways hwo such a model can be created:

  • 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

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).
  • Click napari-czann-segment: Segment with CZANN model in the "Plugins" menu.
  • Select a czann file to use the model for segmentation.
  • metadata of the model will be shown (see example below)
model_typeModelType.SINGLE_CLASS_SEMANTIC_SEGMENTATIONsee: czmodel for details
input_shape[1024, 1024, 1]tile dimensions of model input
output_shape[1024, 1024, 3]tile dimensions of model output
model_idba32bc6d-6bc9-4774-8b47-20646c7cb838unique GUID for that model
min_overlap[128, 128]tile overlap used during training (for this model)
classes['background', 'grains', 'inclusions']available classes
model_nameAPEER-trained modelname of the model

Napari - Image loaded and czann selected

  • Adjust the minimum overlap for the tiling (optional, see cztile for details).
  • Select the layer to be segmented.
  • Toggle Use GPU for inference checkbox to enable / disable using a GPU (Nvidia) for the segmentation (experimental feature).
  • 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 a 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 supported but require the ONNX-GPU runtime and the respective CUDA libraries.
  • Please check the YAML for an example environment with GPU support.
  • See also pytorch for instruction on how to install pytorch

For developers

  • 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 following command:

pip install -e .

To install latest development version:

pip install git+


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.


  • 0.0.18

Last updated:

  • 22 September 2023

First released:

  • 11 July 2022


Supported data:

  • Information not submitted

Plugin type:

GitHub activity:

  • Stars: 7
  • Forks: 1
  • Issues + PRs: 0

Python versions supported:

Operating system:


  • numpy
  • magicgui
  • qtpy
  • napari
  • cztile
  • czmodel[pytorch] >=5
  • onnxruntime-gpu
  • aicsimageio
  • pytest

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