Introducing Track Correction in Celldetective
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Celldetective now empowers users to edit trajectories directly in napari, marking a significant step forward in trajectory refinement and analysis.
Previously, users could view the raw bTrack output in napari, where cell masks were dynamically relabeled to ensure consistent mask values for the same cells across all frames. Now, with the new **Track Correction** feature, you can seamlessly edit trajectories:
1. Select & Edit with Precision: Use the pipette tool to pick a cell mask value, navigate to the next frame, and assign that value to any target cell.
2. Automatic Propagation: The new value propagates across all time points for the selected cell, ensuring consistency.
3. Dynamic Relabeling: If another cell shares the selected value, it is automatically reassigned the next available ``TRACK_ID``, effectively creating a new, distinct trajectory.
This functionality enables intuitive rewiring of tracking branches and the automatic initialization of new tracks for separated branches. Upon saving, trajectory tables are instantly updated to reflect the changes, maintaining compatibility with existing post-processing workflows. From there, you can proceed with measurements or remeasurements effortlessly.
With track correction, Celldetective enhances your ability to refine and analyze cell trajectories, unlocking new possibilities for advanced tracking and accurate data interpretation.
Notable changes
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New Features and Enhancements
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1. **Survival Function for Cell Pairs**: Event times can now be derived from cell pairs or individual partners, enabling synchronized survival analysis. Example: An effector cell forms a synapse with a target cell at $t_\textrm{syn}$ (pair event), and the target dies at $t_\textrm{death}$. Now, you can plot the survival of the target cell synchronized to $t_\textrm{syn}$.
2. **Radial Distance Measurement**: Radial distance to the image center is now measured automatically, simplifying edge-exclusion tasks during analysis.
3. **Dynamic Intensity Feature Handling**: The intensity_mean feature now switches automatically to intensity_nanmean when required, with column names updated accordingly.
4. **Edge-Censoring for First Detection Events**: Cells appearing near image edges are now left-censored for their first detection event. Rationale: Tracks starting close to the edge likely represent cells entering the frame, not sedimenting.
5. **Pre-Event Option for Irreversible Events**: Define pre-requisite events for your primary event of interest.
Cells without the pre-event are assigned NaN for the main event until the pre-event time, ensuring robust event classification.
6. **Priority-Based Time Series Selection**: A hierarchical system for time series selection in event detection models: First preference: Exact match with time series name. Second preference: Name starts with the requirement. Third preference: Requirement appears anywhere in the name.
7. **BigTIFF Encoding for Large Images**: Preprocessing now uses BigTIFF encoding to avoid stack errors for large images (> 4 GB).
Improvements and Fixes
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1. Colormap Bug Resolved: Improved stability and accuracy of visualizations.
2. Automatic Tight Layouts for Figures: Widgets now resize dynamically with tight layouts applied for cleaner visuals.
3. General Bug Fixes: Addressed various minor bugs to enhance usability and performance.
These updates aim to refine workflows, improve data quality, and expand Celldetective’s analytical capabilities. Upgrade now to explore these features!
**Full Changelog**: https://github.com/celldetective/celldetective/compare/v1.3.4.post1...v1.3.5