AIMC Topic: Microscopy, Fluorescence
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MLDA-Net: Multi-Level Deep Aggregation Network for 3D Nuclei Instance Segmentation.
IEEE journal of biomedical and health informatics
May 6, 2025
Segmentation of cell nuclei from three-dimensional (3D) volumetric fluorescence microscopy images is crucial for biological and clinical analyses. In recent years, convolutional neural networks have become the reliable 3D medical image segmentation s...
Single Molecule Localization Super-resolution Dataset for Deep Learning with Paired Low-resolution Images.
Scientific data
Apr 23, 2025
Deep learning super-resolution microscopy has advanced rapidly in recent years. Super-resolution images acquired by single molecule localization microscopy (SMLM) are ideal sources for high-quality datasets. However, the scarcity of public datasets l...
Predicting cell cycle stage from 3D single-cell nuclear-stained images.
Life science alliance
Apr 3, 2025
The cell cycle governs the proliferation of all eukaryotic cells. Profiling cell cycle dynamics is therefore central to basic and biomedical research. However, current approaches to cell cycle profiling involve complex interventions that may confound...
Simultaneous detection of trace protein biomarkers from a single drop of blood using AI-enhanced smartphone-based digital microscopy.
Biosensors & bioelectronics
Feb 13, 2025
The detection of early-stage diseases is often impeded by the low concentrations of protein biomarkers, necessitating sophisticated and costly technologies. In response, we have developed an advanced cyber-physical system that integrates blood plasma...
Deep Learning and Single-Molecule Localization Microscopy Reveal Nanoscopic Dynamics of DNA Entanglement Loci.
ACS nano
Feb 4, 2025
Understanding molecular dynamics at the nanoscale remains challenging due to limitations in the temporal resolution of current imaging techniques. Deep learning integrated with Single-Molecule Localization Microscopy (SMLM) offers opportunities to pr...
UNET-FLIM: A Deep Learning-Based Lifetime Determination Method Facilitating Real-Time Monitoring of Rapid Lysosomal pH Variations in Living Cells.
Analytical chemistry
Feb 4, 2025
Lifetime determination plays a crucial role in fluorescence lifetime imaging microscopy (FLIM). We introduce UNET-FLIM, a deep learning architecture based on a one-dimensional U-net, specifically designed for lifetime determination. UNET-FLIM focuses...
Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning.
Communications biology
Feb 3, 2025
The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigated label-free multimodal nonlinear optical microscopy for non-perturbative profiling of biopharmaceu...
A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.
Nature methods
Jan 27, 2025
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...
Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy.
Nature communications
Jan 2, 2025
Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisit...
Deep learning-enabled filter-free fluorescence microscope.
Science advances
Jan 1, 2025
Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the comple...
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