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Visualizing Yeast Organelles with Fluorescent Protein Markers
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Enhancing yeast cell tracking with a time-symmetric deep learning approach.
Gergely Szabó1, Paolo Bonaiuti2, Andrea Ciliberto3,2
1ITK, PPCU, Práter st. 50/A, Budapest, 1083, Hungary. szabo.gergely@itk.ppke.hu.
NPJ Systems Biology and Applications
|March 14, 2025
Summary
This study introduces a novel deep-learning cell tracking method that analyzes spatio-temporal neighborhoods, not just consecutive frames. This approach improves live cell tracking accuracy and handles complex video data effectively.
Area of Science:
- Cell biology
- Bioimage analysis
- Computational biology
Background:
- Accurate live cell tracking in video microscopy is crucial but challenging for current image processing methods.
- Existing deep-learning approaches often rely on consecutive frames, limiting generalizability.
- Challenges include handling artifacts and learning complex motion patterns.
Purpose of the Study:
- To develop a novel deep-learning-based cell tracking method.
- To overcome limitations of consecutive frame-based tracking.
- To enable learning of cell motion patterns without prior assumptions.
Main Methods:
- A novel deep-learning framework for cell tracking.
- Utilizes spatio-temporal neighborhood analysis instead of consecutive frames.
- Learns cell motion patterns intrinsically.
Main Results:
- Demonstrated efficacy through biologically motivated validation.
- Outperformed multiple state-of-the-art cell tracking methods.
- Successfully applied to budding yeast recordings and simulated samples.
Conclusions:
- The proposed method offers improved accuracy and generalizability for live cell tracking.
- Spatio-temporal neighborhood analysis is a promising approach for deep-learning-based tracking.
- The method shows potential for handling challenging microscopy data with artifacts.
