AIMC Topic: Cell Tracking
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Tracking Multiple Video Targets with an Improved GM-PHD Tracker.
Sensors (Basel, Switzerland)
Dec 3, 2015
Tracking multiple moving targets from a video plays an important role in many vision-based robotic applications. In this paper, we propose an improved Gaussian mixture probability hypothesis density (GM-PHD) tracker with weight penalization to effect...
A multistaged automatic restoration of noisy microscopy cell images.
IEEE journal of biomedical and health informatics
Feb 10, 2014
Automated cell segmentation for microscopy cell images has recently become an initial step for further image analysis in cell biology. However, microscopy cell images are easily degraded by noise during the readout procedure via optical-electronic im...
Deep learning-based enhancement of fluorescence labeling for accurate cell lineage tracing during embryogenesis.
Bioinformatics (Oxford, England)
Nov 1, 2024
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...
MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.
Bioinformatics (Oxford, England)
Mar 29, 2024
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...
Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
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...
pcnaDeep: a fast and robust single-cell tracking method using deep-learning mediated cell cycle profiling.
Bioinformatics (Oxford, England)
Oct 14, 2022
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 ...
Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis.
Stem cell reports
May 11, 2021
Lineage tracing is a powerful tool in developmental biology to interrogate the evolution of tissue formation, but the dense, three-dimensional nature of tissue limits the assembly of individual cell trajectories into complete reconstructions of devel...
A Cell Segmentation/Tracking Tool Based on Machine Learning.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2019
The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (se...
Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.
Advances in experimental medicine and biology
Jan 1, 2016
High content analysis (HCA) has become a leading methodology in phenotypic drug discovery efforts. Typical HCA workflows include imaging cells using an automated microscope and analyzing the data using algorithms designed to quantify one or more spec...
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