Deep convolutional neural network for single-cell image analysis (Conference Presentation)

2018 ◽  
Author(s):  
Edmund Y. Lam ◽  
Nan Meng ◽  
Hayden K. H. So
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Single Cell ◽  
Deep Convolutional Neural Network ◽  
Cell Image ◽  
Cell Image Analysis

scholarly journals Automated urine cell image analysis with a convolutional neural network

2020 ◽  
Vol 19 ◽  
pp. e1357
Author(s):  
M. Kaneko ◽  
K. Tsuji ◽  
K. Masuda ◽  
K. Ueno ◽  
K. Henmi ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Cell Image ◽  
Cell Image Analysis

scholarly journals ME-VAE: Multi-Encoder Variational AutoEncoder for Controlling Multiple Transformational Features in Single Cell Image Analysis

2021 ◽  
Author(s):  
Luke Ternes ◽  
Mark Dane ◽  
Marilyne Labrie ◽  
Gordon Mills ◽  
Joe Gray ◽  
...  
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Imaging Features ◽  
Phenotypic Differences ◽  
Cell Image ◽  
Intensity Measurements ◽  
Quantitative Measurements ◽  
Variational Autoencoder ◽  
Cell Image Analysis ◽  
Organizational Features

AbstractImage-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenging since there are many obstacles, including segmentation and identifying subcellular compartments for feature extraction. Variational autoencoder (VAE) approaches produce encouraging results by mapping from an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to the intrinsic amount of uninformative variability. Herein, we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features. We show that the proposed architecture improves analysis by making distinct populations more separable compared to traditional VAEs and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other modalities.

scholarly journals Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3362 ◽  
Author(s):  
Shang Shang ◽  
Ling Long ◽  
Sijie Lin ◽  
Fengyu Cong
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Deep Convolutional Neural Network ◽  
Microscopic Image ◽  
Bright Field ◽  
Microscopic Images ◽  
Phenotype Classification ◽  
Microscopic Image Analysis

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.

Single-cell image analysis to assess ABC-transporter-mediated efflux in highly purified hematopoietic progenitors

Cytometry ◽  
2002 ◽  
Vol 49 (4) ◽  
pp. 135-142 ◽  
Author(s):  
H.G.P. Raaijmakers ◽  
G. van den Bosch ◽  
J. Boezeman ◽  
T. de Witte ◽  
R.A.P. Raymakers
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Abc Transporter ◽  
Hematopoietic Progenitors ◽  
Cell Image ◽  
Cell Image Analysis

Cell-on-hydrogel platform made of agar and alginate for rapid, low-cost, multidimensional test of antimicrobial susceptibility

Lab on a Chip ◽  
2016 ◽  
Vol 16 (16) ◽  
pp. 3130-3138 ◽  
Author(s):  
Han Sun ◽  
Zhengzhi Liu ◽  
Chong Hu ◽  
Kangning Ren
Keyword(s):  
Image Analysis ◽  
Shear Flow ◽  
Single Cell ◽  
Antimicrobial Susceptibility ◽  
Synergistic Effects ◽  
Low Cost ◽  
Quick Test ◽  
Cell Image ◽  
Cell Image Analysis

Incorporating microfluidics into plate culture, this inexpensive platform generates stable 2D gradients of drugs for testing their synergistic effects. Culturing sample on top of the device eliminates the concern of shear flow, enables convenient collection of cells, and allows quick test based on single-cell image analysis.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Cell Systems ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 608-621
Author(s):  
Mojca Mattiazzi Usaj ◽  
Clarence Hue Lok Yeung ◽  
Helena Friesen ◽  
Charles Boone ◽  
Brenda J. Andrews
Keyword(s):  
Image Analysis ◽  
Single Cell ◽  
Cell Heterogeneity ◽  
Cell Image ◽  
Cell Image Analysis
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