Infer sub-Cellular Object Npe2 plugin

organelle-segmenter-plugin

A plugin that enables organelle segmentation, forked from tools from Allen Institute for Cell Science

License BSD-3 PyPI Python Version tests codecov napari hub

🚧 WIP 🚧 A plugin that enables image segmentation of organelles from linearly-unmixed florescence images based on the segmenter tools provided by Allen Institute for Cell Science.

A napari plugin to infer subcellular components leveraging infer-subc and aics-segmenter

GOAL

To measure shape, position, size, and interaction of organelles/cellular components (Nuclei (nuc, NU), Nucleus (N1), Lysosomes (LS), Mitochondria (mito, MT), Golgi (GL), Peroxisomes (perox, PO), Endoplasmic Reticulum (ER), Lipid Droplet (LD), Cellmask (soma, cellmask), and cytoplasm (cyto, CT) ) during differentiation of iPSCs, in order to understand the Interactome / Spatiotemporal coordination.

🚧 WIP 🚧

Forked from Allen Institute for Cell Science project

The Allen Cell & Structure Segmenter plugin for napari, from which this projects is forked, provides an intuitive graphical user interface to access the powerful segmentation capabilities of an open source 3D segmentation software package developed and maintained by the Allen Institute for Cell Science (classic workflows only with v1.0). ​The Allen Cell & Structure Segmenter is a Python-based open source toolkit developed at the Allen Institute for Cell Science for 3D segmentation of intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes.

More details about Segmenter can be found at https://allencell.org/segmenter


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

Installation 🚧 WIP 🚧

organelle_segmenter_plugin is available on PyPI via:

pip install organelle_segmenter_plugin

Option 2 🚧 COMING SOON 🚧 (not yet available on napari hub)

After you installed the lastest version of napari, you can go to "Plugins" --> "Install/Uninstall Package(s)". Then, you will be able to see all available napari plugins and you can find us by name organelle-segmenter-plugin. Just click the "install" button to install the Segmenter plugin.

Option 3: clone repo + editable install

git clone https://github.com/ndcn/organelle-segmenter-plugin.git
cd organelle-segmenter-plugin
pip install -e .

Quick Start

In the current version, there are two parts in the plugin: workflow editor and batch processing. The workflow editor allows users adjusting parameters in all the existing workflows in the lookup table, so that the workflow can be optimized on users' data. The adjusted workflow can be saved and then applied to a large batch of files using the batch processing part of the plugin.

  1. Open a file in napari by dragging multi-channel .czi file onto napari which will import a multi-channel, multi-Z 'layer'. (Using the menu's defaults to aicsIMAGEIO reader which automatically splits mutliple channels into individual layers. The plugin is able to support multi-dimensional data in .tiff, .tif. ome.tif, .ome.tiff, .czi)
  2. Start the plugin (open napari, go to "Plugins" --> "organelle-segmenter-plugin" --> "workflow editor")
  3. Select the image and channel to work on
  4. Select a workflow based on the example image and target segmentation based on user's data. Ideally, it is recommend to start with the example with very similar morphology as user's data.
  5. Click "Run All" to execute the whole workflow on the sample data.
  6. Adjust the parameters of steps, based on the intermediate results. A complete list of all functions can be found here🚧 WIP 🚧
  7. Click "Run All" again after adjusting the parameters and repeat step 6 and 7 until the result is satisfactory.
  8. Save the workflow
  9. Close the plugin and open the batch processing part by (go to "Plugins" --> "organelle-segmenter-plugin" --> "batch processing")
  10. Load the customized workflow saved above
  11. Load the folder with all the images to process
  12. Click "Run"
  13. Follow the examples in the infer_subc repo for postprocessing of the saved segmentations and generating the statistics.

Contributing

Contributions are very welcome.

License

Distributed under the terms of the BSD-3 license, "organelle-segmenter-plugin" is free and open source software

Issues

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

Version:

  • 0.0.5

Last updated:

  • 27 August 2023

First released:

  • 13 May 2023

License:

  • BSD-3

Supported data:

  • Information not submitted

Plugin type:

Open extension:

GitHub activity:

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

Python versions supported:

Operating system:

Requirements:

  • napari
  • napari-plugin-engine >=0.1.4
  • aicssegmentation
  • aicsimageio
  • numpy
  • scikit-image
  • aicsimageio >=4.7.0

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