AIMC Topic: Cell Tracking

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Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells.

PLoS computational biology
Deep learning-based methods for identifying and tracking cells within microscopy images have revolutionized the speed and throughput of data analysis. These methods for analyzing biological and medical data have capitalized on advances from the broad...

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...

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...

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...

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...

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...

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...

Tracking cell lineages in 3D by incremental deep learning.

eLife
Deep learning is emerging as a powerful approach for bioimage analysis. Its use in cell tracking is limited by the scarcity of annotated data for the training of deep-learning models. Moreover, annotation, training, prediction, and proofreading curre...

DeepSeed Local Graph Matching for Densely Packed Cells Tracking.

IEEE/ACM transactions on computational biology and bioinformatics
The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial top...

Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB.

BMC molecular and cell biology
BACKGROUND: Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in...