CZANN Segmentation

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

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

Installation¶

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)

Parameter	Value	Explanation
model_type	ModelType.SINGLE_CLASS_SEMANTIC_SEGMENTATION	see: czmodel for details
input_shape	[1024, 1024, 1]	tile dimensions of model input
output_shape	[1024, 1024, 3]	tile dimensions of model output
model_id	ba32bc6d-6bc9-4774-8b47-20646c7cb838	unique GUID for that model
min_overlap	[128, 128]	tile overlap used during training (for this model)
classes	['background', 'grains', 'inclusions']	available classes
model_name	APEER-trained model	name of the model

• 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.

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

Remarks¶

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+https://github.com/sebi06/napari_czann_segment.git

Contributing¶

Contributions and Feedback are very welcome.

License¶

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

Issues¶

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