tracktour 0.1.0rc0

Network flow based tracker with guided error correction

tracktour

tracktour is a simple object tracker based on a network flow linear model. tracktour takes a dataframe of detected objects and solves a linear program (currently using Gurobi, but we will soon add an open source solver interface) to produce tracking results.

tracktour is also a napari plugin! The plugin allows you to solve, curate and evaluate your solution using three main widgets, as described below.

⚠️ tracktour is currently under construction! Its API may change without deprecation warnings.⚠️

About tracktour

tracktour is a purely discrete-optimization-based tracker. It takes the coordinates of detected objects as input, and associates these objects over time to create complete trajectories, including divisions. Tracktour's only parameter is k - the number of neighbours to consider for possible assignment in the next frame. Using this parameter and very simple distance based cost, a candidate graph is created, and passed to Gurobi for solving. Once solved, the detected objects and edges that make up the tracks are returned to the user for inspection.

Installation

tracktour is available as a pip-installable Python package. Running pip install tracktour in a virtual environment will install all required dependencies, but you will need a separate Gurobi Optimizer installation (instructions here).

tracktour is tested with all Python versions >=3.11.

If you wish to use the napari plugin functionality, use pip install tracktour[napari].

napari Plugin

⚠️More detail coming soon! The plugin is a proof-of-concept only.⚠️

https://github.com/user-attachments/assets/70e10c60-c7d9-4a8a-91af-4518271cf021

tracktour is most easily used via its napari plugin interface.

The plugin contains three widgets for interacting with your data:

• Track Solver: takes a segmentation or points layer and produces your tracking solution.
• Merge Explorer: if you allowed your tracks to merge, the Merge Explorer takes you through each merge and allows you to correct them by marking the parents as exiting the frame, or adding new cells that were missed in the segmentation/detection step.
• Track Annotator: allows you to pick a sampling strategy and guides you through annotating and correcting ground truth tracks based on your solution. You're shown one edge at a time, with the filled point representing the source, and the hollow point representing the target. You can move either the source or target point around the image to repair the edge. You can also delete either point to signify no incoming/outgoing edge.

After solving, or curating your solution, you can export it to GEFF at any time from each of the widgets. You can then read the solution back in using napari-geff.

Python Usage

The Tracker object is the interface for producing tracking solutions. Below is a toy example with explicitly defined detections.

# define the coordinates of ten detections across three frames.
coords = [
    (0, 50.0, 50.0),
    (0, 40, 50),
    (0, 30, 57),
    (1, 50, 52),
    (1, 38, 51),
    (1, 29, 60),
    (2, 52, 53),
    (2, 37, 53),
    (2, 28, 64),
]
coords = pd.DataFrame(coords, columns=["t", "y", "x"])

# initialize Tracker object
tracker = Tracker(
    im_shape=(100, 100),    # size of the image detections come from. Affects cost of detections appearing/disappearing
    k_neighbours=2          # number of neighbours to consider for assignment in the next frame (default=10)
)
# solve
tracked = tracker.solve(coords)

The Tracked object contains a copy of the detections, potentially reindexed, and a dataframe of edges that make up the solution. Columns u and v in tracked_edges are direct indices into tracked_detections.

print(tracked.tracked_detections)
print(tracked.tracked_edges)

You may want to convert the solution into a networkx graph for easier manipulation.

solution_graph = tracked.as_nx_digraph()

Extracting Detections

If you're starting from an image segmentation, you can use the get_im_centers or extract_im_centers functions.

If your segmentation is already loaded into a numpy array, use extract_im_centers. The returned detections DataFrame is ready for use with the Tracker.

detections, min_t, max_t, corners = extract_im_centers(segmentation)

If your segmentation is in Cell Tracking Challenge format and lives in single tiffs per frame in a directory, use get_im_centers. This will also return the segmentation as a numpy array.

seg, detections, min_t, max_t, corners = get_im_centers('path/to/01_RES/')

Support

Please feel free to open issues with feature requests, bug reports, questions on usage, etc.

Cell Tracking Challenge

Note: Tracktour was recently submitted to the Cell Tracking Challenge. To use the submission version specifically, install tracktour==0.0.4.