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Cell Tracking

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DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis.

eLife
Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamical biological processes. So far, strong limitations in the ability to quantitatively analyze single-c...

Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks.

PLoS computational biology
Robustness in developing and homeostatic tissues is supported by various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking are powerful in providing direct evidence of cell coordination rules, extracting and c...

pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.

Bioinformatics (Oxford, England)
SUMMARY: Computational methods that track single cells and quantify fluorescent biosensors in time-lapse microscopy images have revolutionized our approach in studying the molecular control of cellular decisions. One barrier that limits the adoption ...

Implementation of transfer learning for the segmentation of human mesenchymal stem cells-A validation study.

Tissue & cell
INTRODUCTION: Stem cell therapy has been gaining interest in the regeneration rather than repair of lost human tissues. However, the manual analysis of stem cells prior to implantation is a cumbersome task that can be automated to improve the efficie...

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

Methods in molecular biology (Clifton, N.J.)
Cell tracking is an essential step in extracting cellular signals from moving cells, which is vital for understanding the mechanisms underlying various biological functions and processes, particularly in organs such as the brain and heart. However, c...

MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

Bioinformatics (Oxford, England)
MOTIVATION: Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains li...

Automatic classification of normal and abnormal cell division using deep learning.

Scientific reports
In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives like the Cell Tracking Challenge driving progress in the field. Most studies focus on regular cell population videos in which c...

Deep learning-based enhancement of fluorescence labeling for accurate cell lineage tracing during embryogenesis.

Bioinformatics (Oxford, England)
MOTIVATION: Automated cell lineage tracing throughout embryogenesis plays a key role in the study of regulatory control of cell fate differentiation, morphogenesis and organogenesis in the development of animals, including nematode Caenorhabditis ele...

Enhancing yeast cell tracking with a time-symmetric deep learning approach.

NPJ systems biology and applications
Accurate tracking of live cells using video microscopy recordings remains a challenging task for popular state-of-the-art image processing-based object tracking methods. In recent years, many applications have attempted to integrate deep-learning fra...

Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.

Scientific reports
Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual ce...