• Version:

    • 0.2.2
  • Release date:

    • 15 October 2021
  • First released:

    • 25 September 2021
  • Development status:

    • 3 - Alpha
  • License:

    • BSD-3-Clause

Github Activity

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1
  • Python versions supported:

    • >=3.7
  • Operating system:

    • OS Independent
  • Requirements:

    • napari-plugin-engine (>=0.1.4)
    • numpy
    • napari
    • scikit-image
    • scipy
    • napari-tools-menu

napari-segment-blobs-and-things-with-membranes

A plugin based on scikit-image for segmenting nuclei and cells based on fluorescent microscopy images with high intensity in nuclei and/or membranes

License PyPI Python Version tests codecov

A plugin based on scikit-image for segmenting nuclei and cells based on fluorescent microscopy images with high intensity in nuclei and/or membranes. The available functions and their user interface based on magicgui are shown below. You can also call these functions as shown in the demo notebook.

Voronoi-Otsu-Labeling

This algorithm uses Otsu's thresholding method in combination with Gaussian blur and a Voronoi-Tesselation approach to label bright objects such as nuclei in an intensity image. The alogrithm has two sigma parameters which allow you to fine-tune where objects should be cut (spot_sigma) and how smooth outlines should be (outline_sigma). This implementation aims to be similar to Voronoi-Otsu-Labeling in clesperanto.

img.png

Seeded Watershed

Starting from an image showing high-intensity membranes and a seed-image where objects have been labeled (e.g. using Voronoi-Otsu-Labeling), objects are labeled that are constrained by the membranes.

img.png

Gaussian blur

Applies a Gaussian blur to an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.

img.png

Subtract background

Subtracts background using scikit-image's rolling-ball algorithm. This might be useful, for example to make intensity of membranes more similar in different regions of an image.

img.png

Threshold Otsu

Binarizes an image using scikit-image's threshold Otsu algorithm, also known as Otsu's method.

img.png

Split touching objects (formerly known as binary watershed).

In case objects stick together after thresholding, this tool might help. It aims to deliver similar results as ImageJ's watershed implementation.

img.png

Connected component labeling

Takes a binary image and produces a label image with all separated objects labeled differently. Under the hood, it uses scikit-image's label function.

img.png


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

Installation

Download, unzip and install napari from its github releases page: img.png

Afterwards, go to the menu Plugins > Install/uninstall plugins... and click on the install button next to napari-segment-blobs-and-things-with-membranes: img.png

You can also install napari-segment-blobs-and-things-with-membranes via pip:

pip install napari-segment-blobs-and-things-with-membranes

Contributing

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.

License

Distributed under the terms of the BSD-3 license, "napari-segment-blobs-and-things-with-membranes" is free and open source software

Issues

If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.

  • Version:

    • 0.2.2
  • Release date:

    • 15 October 2021
  • First released:

    • 25 September 2021
  • Development status:

    • 3 - Alpha
  • License:

    • BSD-3-Clause

Github Activity

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1
  • Python versions supported:

    • >=3.7
  • Operating system:

    • OS Independent
  • Requirements:

    • napari-plugin-engine (>=0.1.4)
    • numpy
    • napari
    • scikit-image
    • scipy
    • napari-tools-menu

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