AIMC Topic: Microscopy, Fluorescence

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Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.

Nature communications Dec 21, 2022
Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at onc...
Lighting Interferometry Machine Learning Microscopy, Fluorescence
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Volumetric imaging of fast cellular dynamics with deep learning enhanced bioluminescence microscopy.

Communications biology Dec 3, 2022
Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensiti...
Mice Microscopy, Fluorescence Deep Learning Cytoplasm Caenorhabditis elegans Cell Nucleus Zebrafish Animals
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Incorporating the image formation process into deep learning improves network performance.

Nature methods Oct 31, 2022
We present Richardson-Lucy network (RLN), a fast and lightweight deep learning method for three-dimensional fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, es...
Deep Learning Image Processing, Computer-Assisted Algorithms Artifacts Microscopy, Fluorescence
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Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes.

Nature biotechnology Oct 6, 2022
The goal when imaging bioprocesses with optical microscopy is to acquire the most spatiotemporal information with the least invasiveness. Deep neural networks have substantially improved optical microscopy, including image super-resolution and restor...
Neural Networks, Computer Deep Learning Microscopy, Fluorescence
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Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging.

Sensors (Basel, Switzerland) Sep 26, 2022
Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixe...
Optical Imaging Deep Learning Algorithms Fluorescence Resonance Energy Transfer Microscopy, Fluorescence
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Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses.

Scientific reports Sep 12, 2022
Cell morphology is profoundly influenced by cellular interactions with microenvironmental factors such as the extracellular matrix (ECM). Upon adhesion to specific ECM, various cell types are known to exhibit different but distinctive morphologies, s...
Fibroblasts Extracellular Matrix Microscopy, Fluorescence Neural Networks, Computer Phenotype
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Development of Deep-Learning-Based Single-Molecule Localization Image Analysis.

International journal of molecular sciences Jun 21, 2022
Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SM...
Microscopy, Fluorescence Single Molecule Imaging Deep Learning Image Processing, Computer-Assisted Algorithms
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MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams.

PloS one Jun 15, 2022
Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap...
Image Processing, Computer-Assisted Microplastics Plastics Deep Learning Bivalvia Microscopy, Fluorescence Animals Humans
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Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy.

Nature communications Jun 8, 2022
Volumetric imaging by fluorescence microscopy is often limited by anisotropic spatial resolution, in which the axial resolution is inferior to the lateral resolution. To address this problem, we present a deep-learning-enabled unsupervised super-reso...
Microscopy, Fluorescence Imaging, Three-Dimensional Anisotropy Deep Learning
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VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images.

Scanning Jun 8, 2022
This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT i...
Endoplasmic Reticulum Image Processing, Computer-Assisted Neural Networks, Computer Microscopy, Fluorescence
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