AIMC Topic: Microscopy, Fluorescence

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Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.

Scientific reports Nov 25, 2021
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy t...
Reproducibility of Results Automation, Laboratory Brain Animals Microscopy, Fluorescence Cell Count Image Processing, Computer-Assisted Deep Learning Neurons Mice
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A machine learning approach for single cell interphase cell cycle staging.

Scientific reports Sep 29, 2021
The cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in...
Single-Cell Analysis Image Processing, Computer-Assisted Mice Humans Animals Microscopy, Fluorescence Datasets as Topic Cell Nucleus Support Vector Machine Cell Line Interphase Intravital Microscopy
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Imaging in focus: An introduction to denoising bioimages in the era of deep learning.

The international journal of biochemistry & cell biology Sep 20, 2021
Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of health and disease. However, imaging of live samples is fundamentally limited by the toxicity of the ...
Humans Animals Algorithms Deep Learning Microscopy, Fluorescence Image Processing, Computer-Assisted Signal-To-Noise Ratio
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Machine learning approach for discrimination of genotypes based on bright-field cellular images.

NPJ systems biology and applications Jul 21, 2021
Morphological profiling is a combination of established optical microscopes and cutting-edge machine vision technologies, which stacks up successful applications in high-throughput phenotyping. One major question is how much information can be extrac...
Microscopy, Fluorescence Machine Learning Genotype
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Asbestos Detection with Fluorescence Microscopy Images and Deep Learning.

Sensors (Basel, Switzerland) Jul 4, 2021
Fluorescent probes can be used to detect various types of asbestos (serpentine and amphibole groups); however, the fiber counting using our previously developed software was not accurate for samples with low fiber concentration. Machine learning-base...
Image Processing, Computer-Assisted United States Asbestos Asbestos, Serpentine Microscopy, Fluorescence Deep Learning
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Use of Convolutional Neural Networks for the Detection of u-Serrated Patterns in Direct Immunofluorescence Images to Facilitate the Diagnosis of Epidermolysis Bullosa Acquisita.

The American journal of pathology Jun 28, 2021
The u-serrated immunodeposition pattern in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of epidermolysis bullosa acquisita (EBA). Due to unfamiliarity with serrated patterns, serration patter...
Humans Epidermolysis Bullosa Acquisita Microscopy, Fluorescence Image Interpretation, Computer-Assisted Neural Networks, Computer Fluorescent Antibody Technique, Direct Sensitivity and Specificity
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Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images.

BMC bioinformatics Jun 15, 2021
BACKGROUND: Automated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convol...
Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Microscopy, Fluorescence
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Deep probabilistic tracking of particles in fluorescence microscopy images.

Medical image analysis Jun 8, 2021
Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluo...
Algorithms Reproducibility of Results Microscopy, Fluorescence Bayes Theorem Humans Neural Networks, Computer
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A Preliminary Study of Deep-Learning Algorithm for Analyzing Multiplex Immunofluorescence Biomarkers in Body Fluid Cytology Specimens.

Acta cytologica Jun 2, 2021
INTRODUCTION: Multiplex biomarker analysis of cytological body fluid specimens is often used to assist cytologists in distiguishing metastatic cancer cells from reactive mesothelial cells. However, evaluating biomarker expression visually may be chal...
Feasibility Studies Diagnosis, Computer-Assisted Cytodiagnosis Adenocarcinoma Reproducibility of Results Predictive Value of Tests Humans Diagnosis, Differential Deep Learning Biomarkers, Tumor Microscopy, Fluorescence Pleural Effusion, Malignant Retrospective Studies Image Processing, Computer-Assisted Fluorescent Antibody Technique
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Neural network strategies for plasma membrane selection in fluorescence microscopy images.

Biophysical journal May 4, 2021
In recent years, there has been an explosion of fluorescence microscopy studies of live cells in the literature. The analysis of the images obtained in these studies often requires labor-intensive manual annotation to extract meaningful information. ...
Microscopy, Fluorescence Cell Membrane Neural Networks, Computer Software Image Processing, Computer-Assisted
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