AIMC Topic: Microscopy, Fluorescence
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A Single-Cell Interrogation System from Scratch: Microfluidics and Deep Learning.
The journal of physical chemistry. B
Nov 15, 2024
Live-cell imaging using fluorescence microscopy enables researchers to study cellular processes in unprecedented detail. These techniques are becoming increasingly popular among microbiologists. The emergence of microfluidics and deep learning has si...
Deep learning method for detecting fluorescence spots in cancer diagnostics via fluorescence in situ hybridization.
Scientific reports
Nov 8, 2024
Fluorescence in Situ Hybridization (FISH) is a technique for macromolecule identification that utilizes the complementarity of DNA or DNA/RNA double strands. Probes, crafted from selected DNA strands tagged with fluorophore-coupled nucleotides, hybri...
Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN).
Biomolecules
Oct 23, 2024
With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image ...
Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning.
Nature communications
Sep 12, 2024
Systemic amyloidosis involves the deposition of misfolded proteins in organs/tissues, leading to progressive organ dysfunction and failure. Congo red is the gold-standard chemical stain for visualizing amyloid deposits in tissue, showing birefringenc...
Deep learning permits imaging of multiple structures with the same fluorophores.
Biophysical journal
Sep 3, 2024
Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can...
Benchmarking robustness of deep neural networks in semantic segmentation of fluorescence microscopy images.
BMC bioinformatics
Aug 20, 2024
BACKGROUND: Fluorescence microscopy (FM) is an important and widely adopted biological imaging technique. Segmentation is often the first step in quantitative analysis of FM images. Deep neural networks (DNNs) have become the state-of-the-art tools f...
A supervised graph-based deep learning algorithm to detect and quantify clustered particles.
Nanoscale
Aug 15, 2024
Considerable efforts are currently being devoted to characterizing the topography of membrane-embedded proteins using combinations of biophysical and numerical analytical approaches. In this work, we present an end-to-end (, human intervention-indepe...
Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy.
Microvascular research
Aug 13, 2024
Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and brain of living subjects. In medical imaging, edge detection is used to accurately identify and deli...
Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.
Microscopy research and technique
Aug 10, 2024
Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography a...
Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis.
Biosensors & bioelectronics
Aug 5, 2024
The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the sing...
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