Amend segmentation and track within napari manually.
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
, useGFP
in the config). For multiple intensity images, separate them with commas (e.g.,GFP, mCherry
) - mask_suffix: suffix of the mask image
- track_suffix: suffix of the tracked object table
- frame_base: index of the first frame (either
0
or1
) - 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
- intensity_suffix: suffix of the intensity image (e.g., for
Napari-amdtrk will modify mask and track files in place. Other files are not affected.
- Open
napari
GUI. File
>Open folder
> chooseAmend segmentation and track
Plugins
>napari-amdtrk: Amend track widget
>Run
- In
layer list
, select thesegm
layer to start editing.
Please check out the demo video here and the sample data (see below).
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.
-
↑ and ↓: toggle different operations
-
enter: run the operation
-
Available to a selected object:
- control + 9: shrink the object mask
- control + 0: expand the object mask
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Please install napari
GUI first:
python -m pip install "napari[all]"
You can install napari-amdtrk
via pip:
pip install napari-amdtrk
Distributed under the terms of the MIT license, "napari-amdtrk" is free and open source software
If you encounter any problems, please [file an issue] along with a detailed description.