Time lapse mode
How to track and analyze objects across time using Time lapse mode
Last updated
How to track and analyze objects across time using Time lapse mode
Last updated
Time lapse mode in NimbusImage provides powerful capabilities for tracking and analyzing objects across time points. This feature is particularly useful for cell tracking, movement analysis, and other time-dependent studies.
When working with time lapse datasets, the Time lapse mode option automatically becomes available in the interface:
Look for the "Time lapse mode" checkbox in the variable navigation panel
Check the box to enable tracking visualization and special time lapse features
When enabled, Time lapse mode shows additional options:
In Time lapse mode, objects are connected across time points to form "tracks" that represent the same biological entity (like a cell) over time.
Tracks appear as connected lines between objects across different time points:
Key features of track visualization:
Current time point: The current time point is highlighted with "Curr T=X" label and typically appears larger
Connected objects: All objects connected to the current object are shown as a track
Time labels: Each object shows its time point (T=1, T=7, etc.). These labels are clickable to take you right to that time point.
Connection colors:
Colored connections: Normal connections between consecutive time points. Each color represents a different track.
Red connections: Connections that skip time points (track connecting from e.g. t=8 to t=10)
Line thickness: Forward-in-time connections are thicker than backward-in-time connections
The "Track window" slider controls how many time points before and after the current time point are shown in the track visualization:
Higher values show more of the track's history and future
Lower values focus on just the immediate connections
Adjust based on the density of your data and analysis needs
The tags field allows you to only do time-lapse analysis on a subset of objects defined by the specified tags.
Delete all timelapse connections will delete all connections made by the "Connect timelapse" tool and also the "Lasso connect".
NimbusImage provides a dedicated tool for connecting objects across time:
Key settings:
Object to connect tag: Select which type of objects to connect (based on their tags)
Connect across gaps: Allows connecting objects even when there are missing time points
Max distance: Maximum pixel distance between objects in consecutive frames to be considered for connection
To use the tool:
Create the Tool from the toolset menu
Configure the settings
Click "COMPUTE" to automatically connect objects based on proximity and settings
One of the most powerful features is the ability to use a lasso tool to manually define tracks:
Create a lasso connect tool from the toolset menu
Draw a lasso around the objects you want to connect across time
NimbusImage will automatically connect them sequentially by time point
This feature is particularly helpful for:
Connecting "orphaned" objects to existing tracks
Creating new tracks from scratch
Fixing tracking errors where automatic tracking failed
Here's an example of connecting up an "orphan" cell at t=8 (orphans are in gray). The track is connected up until t=7 and from t=9 onwards, but sometimes you get a gap in the track because of a missed cell segmentation. Here, we drew in the missing cell manually, but then you want to connect up the track:
Using "Lasso connect", you can just circle (sloppily) all the points in the track, and it will "auto-magically" connect the tracks up sequentially:
Tracks provide an intuitive way to navigate through time:
Click on any point in a track to jump directly to that time point
Use this to quickly inspect the history or future of a specific object
Especially useful when analyzing division events or complex behaviors
Start with automatic connections using the Connect timelapse tool
Review and fix tracks using the lasso connect tool for any errors
Use track window setting to adjust visualization density
Enable "Show labels" to see time point information directly on objects
Create properties to analyze track information (velocity, displacement, etc.)
Once you've established tracks, you can:
Create properties to measure changes over time
Export track data to CSV for further analysis in Python/R
Create snapshots to document specific tracking events
Time lapse mode is particularly useful for:
Cell migration tracking
Mitotic event analysis
Particle movement studies
Growth measurements over time
Interaction analysis between objects
By combining NimbusImage's flexible object tagging system with Time lapse mode, you can create sophisticated tracking analyses that capture the dynamic nature of your biological systems.
Download time lapse movies showing your tracks (see for more details)