Temperature dependent geometry in perovskite microcrystals for whispering gallery and Fabry–Pérot mode lasing

2019 ◽  
Vol 7 (14) ◽  
pp. 4102-4108 ◽  
Author(s):  
Bobo Li ◽  
Taojie Zhou ◽  
Xuan Fang ◽  
Weilin Zhang ◽  
Xiaomeng Li ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Vertical Direction ◽  
Lasing Spectrum ◽  
Temperature Dependent ◽  
Cubic Crystal ◽  
Whispering Gallery ◽  
Fabry Perot ◽  
Microscopy Images

The F–P mode lasing spectrum and the fluorescence microscopy images recorded in the vertical direction of a single quasi-cubic crystal.

scholarly journals Quantitative Segmentation of Fluorescence Microscopy Images of Heterogeneous Tissue: Application to the Detection of Residual Disease in Tumor Margins

PLoS ONE ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. e66198 ◽  
Author(s):  
Jenna L. Mueller ◽  
Zachary T. Harmany ◽  
Jeffrey K. Mito ◽  
Stephanie A. Kennedy ◽  
Yongbaek Kim ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Residual Disease ◽  
Tumor Margins ◽  
Heterogeneous Tissue ◽  
Microscopy Images

scholarly journals Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

PLoS ONE ◽  
2008 ◽  
Vol 3 (4) ◽  
pp. e1997 ◽  
Author(s):  
Alfredo Rodriguez ◽  
Douglas B. Ehlenberger ◽  
Dara L. Dickstein ◽  
Patrick R. Hof ◽  
Susan L. Wearne
Keyword(s):  
Fluorescence Microscopy ◽  
Dendritic Spines ◽  
Three Dimensional ◽  
Shape Classification ◽  
Microscopy Images

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.

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