Here we provide some examples of how to use Ultrack for cell tracking.
Some examples are provided as Jupyter notebooks with additional documentation, but we do not recommend using Jupyter notebooks for your day-to-day analysis.
Other examples as Python scripts can be found in here.
Additional packages might be required. Therefore, conda environment files are provided, which can be installed using:
conda env create -f <environment-file.yml>
conda activate <your new env>
pip install git+https://github.com/royerlab/ultrackThe existing examples are:
- multi_color_ensemble : Multi-colored cytoplasm cell tracking using Cellpose and Watershed segmentation ensemble. Data provided by The Lammerding Lab.
- flow_field_3d : Tracking demo on a cartographic projection of Tribolium Castaneum embryo from the cell-tracking challenge, using a flow field estimation to assist tracking of motile cells.
- stardist_2d : Tracking demo on HeLa GPF nuclei from the cell-tracking challenge using Stardist 2D fluorescence images pre-trained model.
- zebrahub : Tracking demo on zebrafish tail data from zebrahub acquired with DaXi using Ultrack's image processing helper functions.
- neuromast_plantseg : Tracking demo membrane-labeled zebrafish neuromast from Jacobo Group of CZ Biohub using PlantSeg's membrane detection model.
- micro_sam : Tracking demo with MicroSAM instance segmentation package.
To run all the examples and update the notebooks in headless mode, run:
bash refresh_examples.sh