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a plugin for mother machine image analysis

Project description


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A plugin for Mother Machine Image Analysis by Jun Lab.

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


We describe installation with mamba, a faster version of conda which we recommend. Installation with conda is the exact same, except replace mamba with conda Run the following command:

mamba create -n napari-mm3 -c conda-forge conda-build tensorflow napari

Now, you need to install our code (please let us know if this causes problems -- it has been a pain point in the past). To do so, clone the repository. Then, run the following commands from within your conda environment:

cd napari-mm3
pip install -e .

This supplies you with the latest, most recent version of our code.

If you would like to have a more stable verison, simply run pip install napari-mm3. In general, we recommend going off of the github version.

NOTES: Not running the conda command above and trying to install things in a different way may lead to difficult issues with PyQt5. We recommend following the above commands to simplify the situation. Using pip -e . instead of mamba develop . is a deliberate choice, the former did not seem to register the plugin with napari.


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.

Usage guide

Brief video introduction: available here

a. Preprocessing

  • TIFFConverter -- Turn your nd2 microscopy data, or other format via bioformats, into TIFFs. If your data is not in the nd2 format, follow the input image guidelines. Make sure to set 'image source' in Compile to 'Other'.

  • Compile -- Locate traps, separate their timelapses into their own TIFFs, and return metadata.

b. Segmentation

With Otsu's method:

  • PickChannels -- User guided selection of empty and full traps.

  • Subtract -- Remove (via subtraction) empty traps from the background of traps that contain cells; run this on the phase contrast channel.

  • SegmentOtsu -- Use Otsu segmentation to segment cells.

With U-Net:

  • Annotate -- annotate images for ML (U-Net or similar) training purposes.

  • Train U-Net -- Train a U-Net model for cell segmentation.

  • SegmentUnet -- Run U-Net segmentation (you will need to supply your own model)

c. Tracking

  • Track -- Acquire individual cell properties and track lineages.

d. Fluorescence data analysis

  • PickChannels -- If you've already done this (e.g. for otsu segmentation), no need to do it again. User guided selection of empty and full traps.

  • Subtract -- Remove (via subtraction) empty traps from the background of traps that contain cells. This time, run this on your fluorescence channels.

  • Colors -- Calculate fluorescence information.

e. Focus tracking

  • Foci -- We use this to track `foci' (bright fluorescent spots) inside of cells.

f. Outputs, inputs, and file structure

Finally, to better understand the data formats, you may wish to refer to the following documents:


Distributed under the terms of the BSD-3 license, "napari-mm3" is free and open source software


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

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