scholarly journals Visualizing topical drug uptake with conventional fluorescence microscopy and deep learning

2020 ◽  
Vol 11 (12) ◽  
pp. 6864
Author(s):  
Conor L. Evans ◽  
Maiko Hermsmeier ◽  
Akira Yamamoto ◽  
Kin F. Chan
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Drug Uptake ◽  
Conventional Fluorescence Microscopy

scholarly journals Modality attention and sampling enables deep learning with heterogeneous marker combinations in fluorescence microscopy

2021 ◽  
Author(s):  
Alvaro Gomariz ◽  
Tiziano Portenier ◽  
Patrick M. Helbling ◽  
Stephan Isringhausen ◽  
Ute Suessbier ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy

scholarly journals Asbestos Detection with Fluorescence Microscopy Images and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4582
Author(s):  
Changjie Cai ◽  
Tomoki Nishimura ◽  
Jooyeon Hwang ◽  
Xiao-Ming Hu ◽  
Akio Kuroda
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Occupational Safety ◽  
Graphics Processing Unit ◽  
Reference Method ◽  
Processing Unit ◽  
Fiber Concentration ◽  
Art Object ◽  
Wide Range ◽  
Microscopy Images

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-based techniques (e.g., deep learning) for image analysis, particularly Convolutional Neural Networks (CNN), have been widely applied to many areas. The objectives of this study were to (1) create a database of a wide-range asbestos concentration (0–50 fibers/liter) fluorescence microscopy (FM) images in the laboratory; and (2) determine the applicability of the state-of-the-art object detection CNN model, YOLOv4, to accurately detect asbestos. We captured the fluorescence microscopy images containing asbestos and labeled the individual asbestos in the images. We trained the YOLOv4 model with the labeled images using one GTX 1660 Ti Graphics Processing Unit (GPU). Our results demonstrated the exceptional capacity of the YOLOv4 model to learn the fluorescent asbestos morphologies. The mean average precision at a threshold of 0.5 ([email protected]) was 96.1% ± 0.4%, using the National Institute for Occupational Safety and Health (NIOSH) fiber counting Method 7400 as a reference method. Compared to our previous counting software (Intec/HU), the YOLOv4 achieved higher accuracy (0.997 vs. 0.979), particularly much higher precision (0.898 vs. 0.418), recall (0.898 vs. 0.780) and F-1 score (0.898 vs. 0.544). In addition, the YOLOv4 performed much better for low fiber concentration samples (<15 fibers/liter) compared to Intec/HU. Therefore, the FM method coupled with YOLOv4 is remarkable in detecting asbestos fibers and differentiating them from other non-asbestos particles.

scholarly journals Phase separation at the nanoscale quantified by dcFCCS

2020 ◽  
Vol 117 (44) ◽  
pp. 27124-27131
Author(s):  
Sijia Peng ◽  
Weiping Li ◽  
Yirong Yao ◽  
Wenjing Xing ◽  
Pilong Li ◽  
...  
Keyword(s):  
Phase Separation ◽  
Detection Limit ◽  
Fluorescence Microscopy ◽  
Critical Concentration ◽  
Transition Process ◽  
Correlation Spectroscopy ◽  
Cellular Processes ◽  
Conventional Fluorescence Microscopy ◽  
Micrometer Scale ◽  
Liquid Liquid Phase Separation

Liquid–liquid phase separation, driven by multivalent macromolecular interactions, causes formation of membraneless compartments, which are biomolecular condensates containing concentrated macromolecules. These condensates are essential in diverse cellular processes. Formation and dynamics of micrometer-scale phase-separated condensates are examined routinely. However, limited by commonly used methods which cannot capture small-sized free-diffusing condensates, the transition process from miscible individual molecules to micrometer-scale condensates is mostly unknown. Herein, with a dual-color fluorescence cross-correlation spectroscopy (dcFCCS) method, we captured formation of nanoscale condensates beyond the detection limit of conventional fluorescence microscopy. In addition, dcFCCS is able to quantify size and growth rate of condensates as well as molecular stoichiometry and binding affinity of client molecules within condensates. The critical concentration to form nanoscale condensates, identified by our experimental measurements and Monte Carlo simulations, is at least several fold lower than the detection limit of conventional fluorescence microscopy. Our results emphasize that, in addition to micrometer-scale condensates, nanoscale condensates are likely to play important roles in various cellular processes and dcFCCS is a simple and powerful quantitative tool to examine them in detail.

Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy

2019 ◽  
Author(s):  
Gouthamrajan Nadarajan ◽  
Tyna Hope ◽  
Dan Wang ◽  
Allison Cheung ◽  
Fiona Ginty ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Ground Truth

scholarly journals Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

2019 ◽  
Vol 16 (12) ◽  
pp. 1323-1331 ◽  
Author(s):  
Yichen Wu ◽  
Yair Rivenson ◽  
Hongda Wang ◽  
Yilin Luo ◽  
Eyal Ben-David ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Three Dimensional ◽  
Microscopy Images

3D high resolution generative deep-learning network for fluorescence microscopy imaging

2020 ◽  
Vol 45 (7) ◽  
pp. 1695 ◽  
Author(s):  
Hang Zhou ◽  
Ruiyao Cai ◽  
Tingwei Quan ◽  
Shijie Liu ◽  
Shiwei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Fluorescence Microscopy ◽  
Microscopy Imaging ◽  
Learning Network ◽  
Deep Learning Network
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