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Cell Tracking

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A multistaged automatic restoration of noisy microscopy cell images.

IEEE journal of biomedical and health informatics
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...

Tracking Multiple Video Targets with an Improved GM-PHD Tracker.

Sensors (Basel, Switzerland)
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...

Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

BioMed research international
In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically ...

Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.

Advances in experimental medicine and biology
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...

Autofocusing and Polar Body Detection in Automated Cell Manipulation.

IEEE transactions on bio-medical engineering
Autofocusing and feature detection are two essential processes for performing automated biological cell manipulation tasks. In this paper, we have introduced a technique capable of focusing on a holding pipette and a mammalian cell under a bright-fie...

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

PLoS computational biology
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a micr...

Analysis of live cell images: Methods, tools and opportunities.

Methods (San Diego, Calif.)
Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug ...

Machine learning applications in cell image analysis.

Immunology and cell biology
Machine learning (ML) refers to a set of automatic pattern recognition methods that have been successfully applied across various problem domains, including biomedical image analysis. This review focuses on ML applications for image analysis in light...