napari-amdtrk 1.0.0

Manually amend segmentation and track within napari

napari-amdtrk

Amend segmentation and track within napari manually.

:eyes: watch a demo video

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Input data structure

Napari-amdtrk reads an input directory which includes:

• An intensity image (tif) in txyc (or txy) format

• An object mask (tif) in txy format

• An object table (csv) with following essential columns:

  • frame: time frame
  • trackId: ID of the track, starting from 1
  • Center_of_the_object_0: x coordinate
  • Center_of_the_object_1: y coordinate
  • continuous_label: the corresponding label (intensity value) of the object in the object mask (You may use skimage.measure.label to get it from a binary mask.)

• A config file named config.yaml (other names are not allowed)

  Within the config file, there should be:

  • intensity_suffix: suffix of the intensity image (e.g., for foo_GFP.tif, use GFP in the config)
  • mask_suffix: suffix of the mask image
  • track_suffix: suffix of the tracked object table
  • frame_base: index of the first frame (either 0 or 1)
  • stateCol: optional column name for the cell state (e.g., cell cycle phase) in the object table. Leave blank if the object table does not contain it.

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Quick start

1. Open napari GUI.
2. File > Open folder > choose Amend segmentation and track
3. Plugins > napari-amdtrk: Amend track widget > Run
4. In layer list, select the segm layer to start editing.

Please check out the demo video here and the sample data (see below).

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Sample data

To load sample data, File > Open Sample > napari-amdtrk > basic tracks or complete cell cycle tracks.

• basic tracks: simple cell tracks as essential input data.
• complete cell cycle tracks: cell tracks with additional cell cycle features.

The above operations will download data to ~/.amd_trk/_sample_data/ (~230MB). After downloading is finished, sample data will be loaded.

Notes

• Please cite this repository if using the plugin in your work (try About > Cite this repository upper right of this homepage).

• Sample data (cell track videos) have been published with pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling. We acknowledge Dr Kuan Yoow Chan and members of his lab for generating the data.

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

Installation

Please install napari GUI first:

python -m pip install "napari[all]"

You can install napari-amdtrk via pip:

pip install napari-amdtrk

License

Distributed under the terms of the MIT license, "napari-amdtrk" is free and open source software

Issues

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