AIMC Topic: Cell Tracking
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TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.
Medical image analysis
Jun 26, 2019
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cell lines acquired by quantitative phase ima...
Cell mitosis event analysis in phase contrast microscopy images using deep learning.
Medical image analysis
Jun 22, 2019
In this paper, we solve the problem of mitosis event localization and its stage localization in time-lapse phase-contrast microscopy images. Our method contains three steps: first, we formulate a Low-Rank Matrix Recovery (LRMR) model to find salient ...
Learn to segment single cells with deep distance estimator and deep cell detector.
Computers in biology and medicine
Apr 8, 2019
Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, de...
Tracing cell lineages in videos of lens-free microscopy.
Medical image analysis
Jun 5, 2018
In vitro experiments with cultured cells are essential for studying their growth and migration pattern and thus, for gaining a better understanding of cancer progression and its treatment. Recent progress in lens-free microscopy (LFM) has rendered it...
Robotic Patterning a Superhydrophobic Surface for Collective Cell Migration Screening.
Tissue engineering. Part C, Methods
Mar 27, 2018
Collective cell migration, in which cells migrate as a group, is fundamental in many biological and pathological processes. There is increasing interest in studying the collective cell migration in high throughput. Cell scratching, insertion blocker,...
Machine learning applications in cell image analysis.
Immunology and cell biology
Mar 15, 2017
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...
Analysis of live cell images: Methods, tools and opportunities.
Methods (San Diego, Calif.)
Feb 27, 2017
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 ...
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.
PLoS computational biology
Nov 4, 2016
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...
Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.
BioMed research international
Oct 26, 2016
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 ...
Autofocusing and Polar Body Detection in Automated Cell Manipulation.
IEEE transactions on bio-medical engineering
Jul 13, 2016
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...
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