AIMC Topic: Microscopy, Phase-Contrast
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Characterization of drug effects on cell cultures from phase-contrast microscopy images.
Computers in biology and medicine
Oct 14, 2022
In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging....
Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation.
Magnetic resonance in medicine
Sep 12, 2022
PURPOSE: To develop and validate a deep learning-based reconstruction framework for highly accelerated two-dimensional (2D) phase contrast (PC-MRI) data with accurate and precise quantitative measurements.
Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks.
Nature communications
Sep 9, 2022
X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system's spatial resolution. Here we...
Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging.
IEEE transactions on medical imaging
Oct 27, 2021
Quantitative differential phase-contrast (qDPC) imaging is a label-free phase retrieval method for weak phase objects using asymmetric illumination. However, qDPC imaging with fewer intensity measurements leads to anisotropic phase distribution in re...
Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.
Scientific reports
Sep 30, 2021
Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. ...
Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.
Cells
Sep 29, 2021
In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived fro...
Deep learning classification of lipid droplets in quantitative phase images.
PloS one
Apr 5, 2021
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,...
Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.
Nature communications
Dec 7, 2020
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...
Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network.
Reproductive toxicology (Elmsford, N.Y.)
Nov 26, 2020
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...
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
Sep 23, 2019
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...
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