scholarly journals Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis

2019 ◽  
Author(s):  
D Waithe ◽  
JM Brown ◽  
K Reglinski ◽  
I Diez-Sevilla ◽  
D Roberts ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Augmented Reality ◽  
Fluorescence Microscopy ◽  
Object Detection ◽  
High Performance ◽  
Cell Types ◽  
Detection Algorithms ◽  
Fast Processing ◽  
Automate Detection ◽  
The Cost

AbstractIn this paper we demonstrate the application of object detection networks for the classification and localization of cells in fluorescence microscopy. We benchmark two leading object detection algorithms across multiple challenging 2-D microscopy datasets as well as develop and demonstrate an algorithm which can localize and image cells in 3-D, in real-time. Furthermore, we exploit the fast processing of these algorithms and develop a simple and effective Augmented Reality (AR) system for fluorescence microscopy systems. Object detection networks are well-known high performance networks famously applied to the task of identifying and localizing objects in photography images. Here we show their application and efficiency for localizing cells in fluorescence microscopy images. Object detection algorithms are typically trained on many thousands of images, which can be prohibitive within the biological sciences due to the cost of imaging and annotating large amounts of data. Through taking different cell types and assays as an example, we show that with some careful considerations it is possible to achieve very high performance with datasets with as few as 26 images present. Using our approach, it is possible for relatively non-skilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of conventionally manual fluorescence microscopy acquisition pipelines.

scholarly journals Object detection networks and augmented reality for cellular detection in fluorescence microscopy

2020 ◽  
Vol 219 (10) ◽  
Author(s):  
Dominic Waithe ◽  
Jill M. Brown ◽  
Katharina Reglinski ◽  
Isabel Diez-Sevilla ◽  
David Roberts ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Fluorescence Microscopy ◽  
Object Detection ◽  
High Performance ◽  
Back Projection ◽  
Detection Algorithms ◽  
High Classification Accuracy ◽  
Fast Processing ◽  
Automate Detection ◽  
Very High

Object detection networks are high-performance algorithms famously applied to the task of identifying and localizing objects in photography images. We demonstrate their application for the classification and localization of cells in fluorescence microscopy by benchmarking four leading object detection algorithms across multiple challenging 2D microscopy datasets. Furthermore we develop and demonstrate an algorithm that can localize and image cells in 3D, in close to real time, at the microscope using widely available and inexpensive hardware. Furthermore, we exploit the fast processing of these networks and develop a simple and effective augmented reality (AR) system for fluorescence microscopy systems using a display screen and back-projection onto the eyepiece. We show that it is possible to achieve very high classification accuracy using datasets with as few as 26 images present. Using our approach, it is possible for relatively nonskilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of fluorescence microscopy acquisition pipelines.

Glutathione transport by type II cells in perfused rat lung

1994 ◽  
Vol 267 (4) ◽  
pp. L447-L455 ◽  
Author(s):  
C. Bai ◽  
L. A. Brown ◽  
D. P. Jones
Keyword(s):  
Fluorescence Microscopy ◽  
High Performance ◽  
Cell Types ◽  
Alveolar Type ◽  
Type Ii Cells ◽  
Uptake System ◽  
Type Ii ◽  
Rat Lung ◽  
Perfused Lung ◽  
Type Ii Cell

Glutathione (GSH) is an antioxidant that protects the lung against oxidative-injury. Most cells rely on synthesis of GSH to maintain intracellular supply and only a few cell types take up intact GSH. Although isolated type II cells from rat have a Na(+)-dependent uptake system that transports GSH into the cells against a concentration gradient, it is not known whether this occurs from the vasculature in the intact lung or whether other cell types in the lung also transport GSH. Based on the knowledge that gamma-glutamyl analogues of GSH are also transported by the Na(+)-GSH transporter, a method was developed and used to study the cell specificity of GSH uptake in perfused lung. A stable, fluorescent GSH S-conjugate (GSH-I14) was synthesized and separated from the original dye as analyzed by high-performance liquid chromatography. Studies with isolated alveolar type II cells showed that uptake of GSH-I14 was Na+ dependent and inhibited by GSH. In addition, uptake of GSH by the type II cells was inhibited by GSH-I14. After perfusion of the isolated rat lung with GSH-I14, the conjugate accumulated primarily in the alveolar type II cell as observed by fluorescence microscopy. This was confirmed by isolation of type II cells and measurement of GSH-I14 content. Thus these results show that specificity of GSH transport can be studied with the fluorescent derivative, GSH-I14, and that in the isolated perfused lung type II cells can transport and concentrate GSH-I14 from the perfusate. Quantitative fluorescence microscopy will be required to further determine relative transport activities by other cell types.

Synthesis of 3-(Cyclohexylamino)-2-arylimidazo[1,2-a]pyridine-8- carboxylic Acids Via an Efficient Three-component Condensation Reaction between Cyclohexylisocyanide and 2-Aminopyridine-3-carboxylic Acid in the Presence of Aromatic Aldehyde

2018 ◽  
Vol 21 (4) ◽  
pp. 298-301 ◽  
Author(s):  
Ghasem Marandi
Keyword(s):  
Biological Sciences ◽  
Carboxylic Acid ◽  
Carboxylic Acids ◽  
High Performance ◽  
Condensation Reaction ◽  
Aromatic Aldehydes ◽  
New Materials ◽  
One Pot ◽  
High Yields ◽  
Benzaldehyde Derivatives

Aim and Objective: The reaction of cyclohexylisocyanide and 2-aminopyridine-3- carboxylic acid in the presence of benzaldehyde derivatives in ethanol led to 3-(cyclohexylamino)-2- arylimidazo[1,2-a]pyridine-8-carboxylic acids in high yields. In a three component condensation reaction, isocyanide reacts with 2-aminopyridine-3-carboxylic acid and aromatic aldehydes without any prior activation. Material and Methods: The synthesized products have stable structures which have been characterized by IR, 1H, 13C and Mass spectroscopy as well as CHN-O analysis. Results: In continuation of our attempts to develop simple one-pot routes for the synthesis of 3- (cyclohexylamino)-2-arylimidazo[1,2-a]pyridine-8-carboxylic acids, aromatic aldehydes with divers substituted show a high performance. Conclusion: In conclusion, this study introduces the art of combinatorial chemistry using a simple one-pot procedure for the synthesis of new materials which are interesting compounds in medicinal and biological sciences.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

2021 ◽  
pp. 1-17
Author(s):  
Mohammed Al-Andoli ◽  
Wooi Ping Cheah ◽  
Shing Chiang Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Complex Networks ◽  
Learning Community ◽  
Community Detection ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Detection Algorithms ◽  
Real World Datasets ◽  
The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

Based on Numerical Simulation of High-Performance Parallel Machine Muffler Experimental Calibration

2013 ◽  
Vol 718-720 ◽  
pp. 1645-1650
Author(s):  
Gen Yin Cheng ◽  
Sheng Chen Yu ◽  
Zhi Yong Wei ◽  
Shao Jie Chen ◽  
You Cheng
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Boundary Element ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
Parallel Machine ◽  
Simulation Software ◽  
Experimental Calibration ◽  
The Cost

Commonly used commercial simulation software SYSNOISE and ANSYS is run on a single machine (can not directly run on parallel machine) when use the finite element and boundary element to simulate muffler effect, and it will take more than ten days, sometimes even twenty days to work out an exact solution as the large amount of numerical simulation. Use a high performance parallel machine which was built by 32 commercial computers and transform the finite element and boundary element simulation software into a program that can running under the MPI (message passing interface) parallel environment in order to reduce the cost of numerical simulation. The relevant data worked out from the simulation experiment demonstrate that the result effect of the numerical simulation is well. And the computing speed of the high performance parallel machine is 25 ~ 30 times a microcomputer.

Sign in / Sign up

Export Citation Format

Share Document