Cell tracking example#

This example demonstrates how the track layer is used for visualizing cell tracking data, by displaying 2D + time dataset of cells with colored properties for track-id.

Thanks to Dr. Alessia Ruggieri and Philipp Klein, Centre for Integrative Infectious Disease Research (CIID), University Hospital Heidelberg, Germany for the data. You can find the data on: https://doi.org/10.5281/zenodo.15597019

Tags: visualization-advanced, colored 2D tracks

Python (.py)

Notebook (.ipynb)

Extra packages required

This example requires additional packages that are not available in typical napari installations. For this example to work in recommended napari installations, you will need to install additional packages: trackastra. See the plugin manager guide for ways to install additional packages from within napari.

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from pathlib import Path

import numpy as np
import pooch
import tifffile
from trackastra.model import Trackastra
from trackastra.tracking import ctc_to_napari_tracks, graph_to_ctc

import napari

# Create temporary directory
tmp_dir = Path(pooch.os_cache('napari-cell-tracking-example'))
tmp_dir.mkdir(parents=True, exist_ok=True)

# Extract silver truth data for trackastra
st_url = "doi:10.5281/zenodo.15852284/masks_pred.npz"
st_path = pooch.retrieve(
    url=st_url,
    fname="masks_pred.npz",
    known_hash=None,
    path=pooch.os_cache("napari cell tracking example")
)

# Extract raw tif files for trackastra
tif_url = "doi:10.5281/zenodo.17643282/01(1).zip"
tif_file = pooch.retrieve(
    url=tif_url,
    fname="01(1).zip",
    known_hash=None,
    path=pooch.os_cache("napari_cell_tracking_example"),
    processor=pooch.Unzip(),
)

# Load the downloaded masks
masks_npz = np.load(st_path)
masks = masks_npz['masks']

# Sort through images and stack them
imgs = sorted(tif_file)

images = np.stack([
        tifffile.imread(fn) for fn in imgs
        ])

# Initiate trackastra model
model = Trackastra.from_pretrained("general_2d", device='cpu')

# Generate tracks
graph, *_ = model.track(imgs=images, masks=masks, mode='greedy')
tracks_df, tracked_masks = graph_to_ctc(graph=graph, masks_original=masks)

napari_tracks, napari_graph = ctc_to_napari_tracks(segmentation=tracked_masks, man_track=tracks_df)

# Add Napari viewer
viewer = napari.Viewer()
viewer.add_image(images, name='Images')
viewer.add_labels(masks, name='Predicted Masks')
viewer.add_tracks(napari_tracks, graph=napari_graph, name="Tracks")

if __name__ == "__main__":
    napari.run()

Total running time of the script: (0 minutes 16.328 seconds)

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