How to detect an event using conditions

This guide shows you how to detect time-correlated events (e.g., cell death, spreading) by applying feature-based classification rules that produce a binary signal, which is then fitted with a sigmoid to extract the event time.

Reference keys: event, event class, event time, threshold-based event detection

Prerequisite: You have accurately segmented, tracked, and measured a cell population of interest. This guide only applies to dynamic data.

Define the classification rules

  1. In the Measurements section, click Classify data.

  2. Enter a name for the event (e.g., death).

  3. Project features of interest to identify the transition signal. For example, plot PI_intensity_mean over time to see when cells become PI-positive.

  4. Write the classification condition for the event (e.g., PI_intensity_mean > 500).

  5. Check the Time correlated event option. This triggers sigmoid fitting on the resulting binary signal.

  6. Click Apply.

How the sigmoid fitting works

The classification condition is evaluated at each time point, producing a binary signal per cell:

  • A completely null signal → no event detected.

  • A completely positive signal → the event occurred before imaging started.

  • A sigmoid-like switch → a transition. The time of event is extracted by fitting a sigmoid. An \(R^2 > 0.7\) validates the fit; otherwise the cell is classified as “else” for manual correction.

static_class

The window to perform a feature-based classification on either static detections or trajectories.

Refine event annotations

After automatic detection, use the Event Annotator to verify and correct event times:

See also

How to annotate an event for a step-by-step guide on manual event annotation and correction.