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Fluorescence Energy Transfer Computing
This chapter presents the concept and implementation of fluorescence energy transfer computing, specifically utilizing Förster resonance energy...
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Spatial redundancy transformer for self-supervised fluorescence image denoising
Fluorescence imaging with high signal-to-noise ratios has become the foundation of accurate visualization and analysis of biological phenomena....
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Reflectance Mode Fluorescence Optical Tomography with Consumer-Grade Cameras
Efficient algorithms for solving inverse optical tomography problems with noisy and sparse measurements are a major challenge for near-infrared...
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Deep Image Prior for Spatio-temporal Fluorescence Microscopy Images DECO-DIP
Image deconvolution and denoising is a common postprocessing step to improve the quality of biomedical fluorescence microscopy images. In recent...
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A Multi-scale Method for Cell Segmentation in Fluorescence Microscopy Images
Accurate segmentation of cells in fluorescent microscopy images plays a key role in high-throughput applications such as the quantification of...
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\(\textsf {Fluo}\) : A Domain-Specific Language for Experiments in Fluorescence Microscopy (Application Paper)
Fluorescence microscopy is a true workhorse in the domain of life sciences, essential for unraveling the inner workings of cells and tissue. It is...
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A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images
Single particle tracking is an important image analysis technique widely used in biomedical sciences to follow the movement of subcellular...
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Fluctuation-Based Deconvolution in Fluorescence Microscopy Using Plug-and-Play Denoisers
The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible...
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Analysis of Extracellular Vesicle Data on Fluorescence and Atomic Force Microscopy Images
Extracellular vesicles (EV) enable cell-to-cell communication in the body of an organism and carry significant potential in the medical field as...
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Optimizing Deep Learning Models for Cell Recognition in Fluorescence Microscopy: The Impact of Loss Functions on Performance and Generalization
In the rapidly evolving domain of fluorescence microscopy, the application of Deep Learning techniques for automatic cell segmentation presents...
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Topological Properties of Mouse Neuronal Populations in Fluorescence Microscopy Images
In this work, we processed sets of images obtained by the light-sheet fluorescence microscopy method. We selected different cell groups and...
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Simulation Modelling and Machine Learning Platform for Processing Fluorescence Spectroscopy Data
A digital computational platform is proposed for processing fluorescence spectroscopy data, which implements complex analysis of experimental...
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Engineering a data processing pipeline for an ultra-lightweight lensless fluorescence imaging device with neuronal-cluster resolution
In working toward the goal of uncovering the inner workings of the brain, various imaging techniques have been the subject of research. Among the...
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Applications of Fluorescence Spectroscopy and Machine Learning Methods for Monitoring of Elimination of Carbon Nanoagents from the Body
AbstractThe study considers the application of artificial neural networks to solve the inverse problem of fluorescence (FL) spectroscopy for...
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Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image
Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and...
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Deep localization of subcellular protein structures from fluorescence microscopy images
Accurate localization of proteins from fluorescence microscopy images is challenging due to the inter-class similarities and intra-class disparities...
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Fluorescence microscopy image noise reduction using IEMD-based adaptive thresholding approach
Fluorescence microscopy is an important investigation tool for discoveries in the field of biological sciences. In this paper, we propose an adaptive...
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Unsupervised Cell Segmentation in Fluorescence Microscopy Images via Self-supervised Learning
Cell segmentation in microscopy images is challenging particularly when only few or no annotations available. Existing unsupervised deep...
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Joint Denoising and Super-Resolution for Fluorescence Microscopy Using Weakly-Supervised Deep Learning
Recent studies have shown that joint denoising and super-resolution (JDSR) approach is capable of producing high-quality medical images. The training...