AIMC Topic: Microscopy, Fluorescence

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Automatic classification and segmentation of single-molecule fluorescence time traces with deep learning.

Nature communications Nov 17, 2020
Traces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we u...
Neural Networks, Computer Single Molecule Imaging Microscopy, Fluorescence Fluorescence Resonance Energy Transfer Algorithms ErbB Receptors Databases, Factual Deep Learning
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Image-based phenotyping of disaggregated cells using deep learning.

Communications biology Nov 13, 2020
The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undes...
Microscopy, Fluorescence Cells Intracellular Space Single-Cell Analysis Image Processing, Computer-Assisted Humans Deep Learning Phenotype Cell Line, Tumor
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Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network.

Communications biology Nov 12, 2020
We propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so nei...
Humans Deep Learning Microscopy, Fluorescence Membrane Proteins Unsupervised Machine Learning Diffusion HeLa Cells MCF-7 Cells Carbocyanines Fluorescent Dyes
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Automated classification of bacterial cell sub-populations with convolutional neural networks.

PloS one Oct 26, 2020
Quantification of phenotypic heterogeneity present amongst bacterial cells can be a challenging task. Conventionally, classification and counting of bacteria sub-populations is achieved with manual microscopy, due to the lack of alternative, high-thr...
Bacillus subtilis Microscopy, Fluorescence Bacterial Load Neural Networks, Computer Algorithms Image Processing, Computer-Assisted
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3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis.

NPJ systems biology and applications Oct 20, 2020
During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To ac...
Embryo, Mammalian Embryonic Development Cell Nucleus Imaging, Three-Dimensional Mice Microscopy, Fluorescence Neural Networks, Computer Animals
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SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

Scientific reports Oct 15, 2020
Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we presen...
Middle Aged Deep Learning Image Processing, Computer-Assisted Female Aged Biomarkers, Tumor Coloring Agents Actins Keratins Staining and Labeling Pancreatic Neoplasms Microscopy, Fluorescence Humans Algorithms Phenotype
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Machine-assisted interpretation of auramine stains substantially increases through-put and sensitivity of microscopic tuberculosis diagnosis.

Tuberculosis (Edinburgh, Scotland) Sep 19, 2020
Of all bacterial infectious diseases, infection by Mycobacterium tuberculosis poses one of the highest morbidity and mortality burdens on humans throughout the world. Due to its speed and cost-efficiency, manual microscopy of auramine-stained sputum ...
Microscopy, Fluorescence Tuberculosis Coloring Agents Mycobacterium tuberculosis Benzophenoneidum Image Processing, Computer-Assisted Staining and Labeling Sputum Humans Retrospective Studies Deep Learning
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Myelin detection in fluorescence microscopy images using machine learning.

Journal of neuroscience methods Sep 12, 2020
BACKGROUND: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS ...
Neural Networks, Computer Myelin Sheath Machine Learning Axons Animals Mice Microscopy, Fluorescence
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Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion.

PLoS computational biology Sep 8, 2020
Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and ...
Eukaryotic Cells Neural Networks, Computer Macrophages Deep Learning Microscopy, Fluorescence THP-1 Cells Image Processing, Computer-Assisted Computational Biology Humans
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ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells.

Theranostics Sep 2, 2020
A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological ma...
Image Processing, Computer-Assisted Pancreatic Neoplasms Predictive Value of Tests Biomarkers, Tumor Humans Male Machine Learning Middle Aged Microscopy, Fluorescence Software Principal Component Analysis Liquid Biopsy Internet of Things Female Neoplastic Cells, Circulating Computer Security Reproducibility of Results Cell Count Aged
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