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Microscopy, Phase-Contrast

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Detection and Recognition for Life State of Cell Cancer Using Two-Stage Cascade CNNs.

IEEE/ACM transactions on computational biology and bioinformatics
Cancer cell detection and its stages recognition of life cycle are an important step to analyze cellular dynamics in the automation of cell based-experiments. In this work, a two-stage hierarchical method is proposed to detect and recognize different...

A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones.

Cancer research
: Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts have been made to train AI using medical images for clinical applications. However, whether AI can di...

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning.

Analytical chemistry
CD8 T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifyi...

Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network.

IEEE transactions on medical imaging
Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state-of-the-art approaches in apoptosis classification use deep convolutional neural networks (CNNs). However, these networks ar...

Cell mitosis event analysis in phase contrast microscopy images using deep learning.

Medical image analysis
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 ...

Sequential Saliency Guided Deep Neural Network for Joint Mitosis Identification and Localization in Time-Lapse Phase Contrast Microscopy Images.

IEEE journal of biomedical and health informatics
The analysis of cell mitotic behavior plays important role in many biomedical research and medical diagnostic applications. To improve the accuracy of mitosis detection in automated analysis systems, this paper proposes the sequential saliency guided...

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Optics express
Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence propert...

Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network.

Reproductive toxicology (Elmsford, N.Y.)
The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep...

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Nature communications
Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown t...

Deep learning classification of lipid droplets in quantitative phase images.

PloS one
We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing,...