Automatic Cut Detector

License BSD-3 PyPI Python Version tests codecov napari hub

Automatic micro-tubules cut detector.

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

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Conda environment

It is highly recommended to create a dedicated conda environment, by following these few steps:

  1. Install an Anaconda distribution of Python. Note you might need to use an anaconda prompt if you did not add anaconda to the path.

  2. Open an Anaconda prompt as admin to create a new environment using conda. We advice to use python 3.10 and conda 23.10.0, to get conda-libmamba-solver as default solver.

conda create --name cut_detector python=3.10 conda=23.10.0
conda activate cut_detector

Package installation

Once in a dedicated environment, our package can be installed via pip:

pip install cut_detector

Alternatively, you can clone the github repo to access to playground scripts.

git clone
cd cut-detector
pip install -e .


We highly recommend to use GPU to speed up segmentation. To use your NVIDIA GPU, the first step is to download the dedicated driver from NVIDIA.

Next we need to remove the CPU version of torch:

pip uninstall torch

The GPU version of torch to be installed can be found here. You may choose the CUDA version supported by your GPU, and install it with conda. This package has been developed with the version 11.6, installed with this command:

conda install pytorch==1.12.1 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia


To update cut-detector to the latest version, open an Anaconda prompt and use the following commands:

conda activate cut_detector
pip install cut-detector --upgrade


Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.


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


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


  • 1.0.1

Last updated:

  • 13 June 2024

First released:

  • 17 October 2023


Supported data:

  • Information not submitted

Plugin type:

  • Information not submitted

GitHub activity:

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

Python versions supported:

Operating system:


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  • pyimagej
  • cnn-framework==0.0.16
  • magicgui
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  • xmltodict
  • shapely
  • aicsimageio==4.14.0
  • fsspec==2023.6.0
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  • napari[all]
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  • numba>=0.59.1