A plugin based on scikit-image for segmenting nuclei and cells based on fluorescent microscopy images with high intensity in nuclei and/or 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. 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.
This algorithm uses Otsu's thresholding method in combination with
Gaussian blur and a
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 (
This implementation aims to be similar to Voronoi-Otsu-Labeling in clesperanto.
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.
Seeded Watershed using local minima as starting points
Similar to the Seeded Watershed and Voronoi-Otsu-Labeling explained above, you can use this tool to segment an image showing membranes without an additional image showing nuclei. The two sigma parameters allow to fine tune how close objects can be and how precise their boundaries are detected.
Applies a Gaussian blur to an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.
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.
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.
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.
Download, unzip and install napari from its github releases page:
Afterwards, go to the menu
Plugins > Install/uninstall plugins... and click on the install button next to
You can also install
napari-segment-blobs-and-things-with-membranes via pip:
pip install napari-segment-blobs-and-things-with-membranes
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, "napari-segment-blobs-and-things-with-membranes" is free and open source software
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