cyto.tracking

Cell tracking algorithms — Fiji/TrackMate integration, in-memory TrackMate, and Trackpy feature-point linking.

class cyto.tracking.trackmate.TrackMate(FIJI_DIR='', ij=None, linking_max_distance=15.0, max_frame_gap=5, gap_closing_max_distance=15.0, pixel_size=1.0, size_min=None, size_max=None, verbose=True, use_in_memory=True)

Bases: object

cyto.tracking.trackmate.main()

TrackMate In-Memory Processing Example

This module demonstrates how to: 1. Load pre-segmented label data and corresponding images 2. Extract cell data to CSV format from labels 3. Use TrackMate for particle tracking via pyimagej scripting

Requirements: - pyimagej - imagej with TrackMate plugin - numpy - pandas - scikit-image - tifffile (for loading TIFF files)

class cyto.tracking.trackmate_in_mem.TrackMateInMemoryProcessor(imagej_path: str | None = None, ij=None)

Bases: object

Handles tracking using pyimagej and TrackMate with pre-segmented data

create_trackmate_model(spots_df: DataFrame, image_shape: Tuple[int, ...], pixel_size: float = 1.0, time_interval: float = 1.0) → Tuple[Any, Any]

Create TrackMate model from spots DataFrame

Parameters:

• spots_df – DataFrame containing spot information

• image_shape – Shape of the original image (height, width) or (frames, height, width)

• pixel_size – Physical pixel size in micrometers

• time_interval – Time interval between frames in minutes

Returns:

Tuple of (model, settings)

export_tracking_results(model: Any, output_dir: Path) → Dict[str, Path]

Export tracking results to CSV format compatible with TrackMate CSV importer

Parameters:

• model – TrackMate model with tracking results

• output_dir – Directory to save results

Returns:

Dictionary with paths to exported files

labels_to_spots_csv(labels: ndarray, image: ndarray, time_points: List[int] | None = None) → DataFrame

Convert label image to CSV format suitable for TrackMate using cyto.utils.label_to_table.label_to_sparse

Parameters:

• labels – Label image from segmentation (XYT format)

• image – Original intensity image (XYT format)

• time_points – List of time points (for time series)

Returns:

DataFrame with spot information in TrackMate format

load_tiff_data(image_path: Path, label_path: Path) → Tuple[ndarray, ndarray]

Load image and label data from TIFF files

Parameters:

• image_path – Path to the original image TIFF file

• label_path – Path to the segmented label TIFF file

Returns:

Tuple of (image_array, label_array)

run_tracking(model: Any, settings: Any) → Dict[str, Any]

Run TrackMate tracking algorithm

Parameters:

• model – TrackMate model

• settings – TrackMate settings

Returns:

Dictionary with tracking results

cyto.tracking.trackmate_in_mem.run_trackmate_on_data()

Main function to run TrackMate tracking on your specific data files

Note

cyto/tracking/trackmate_script_run.py is a Fiji script that uses ImageJ parameter injection (#@ File syntax). It is not importable as a standard Python module and is excluded from this API reference.