Measurement of RBC agglutination with microscopic cell image analysis in a microchannel chip

2014 ◽  
Vol 56 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Chi Hyun Cho ◽  
Ju Yeon Kim ◽  
Agnes E. Nyeck ◽  
Chae Seung Lim ◽  
Dae Sung Hur ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cell Image ◽  
Cell Image Analysis

The use of the digital cell image analysis of Feulgen-stained nuclei to detect apoptosis

1995 ◽  
Vol 104 (5) ◽  
pp. 407-414 ◽  
Author(s):  
Isabelle Camby ◽  
Isabelle Salmon ◽  
Andr� Danguy ◽  
Jean-Lambert Pasteels ◽  
Robert Kiss
Keyword(s):  
Image Analysis ◽  
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.

Cancer Cell Image Analysis and Visualization

2012 ◽  
pp. 233-243
Author(s):  
Tae-Yun Kim ◽  
Hae-Gil Hwang ◽  
Heung-Kook Choi
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Case Studies ◽  
Cancer Cell ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Dimensional Image ◽  
The Past ◽  
Cell Image ◽  
Cell Image Analysis

We review computerized cancer cell image analysis and visualization research over the past 30 years. Image acquisition, feature extraction, classification, and visualization from two-dimensional to three-dimensional image algorithms are introduced with case studies of bladder, prostate, breast, and renal carcinomas.

Cell image analysis for malaria detection using deep convolutional network

2020 ◽  
Vol 14 (1) ◽  
pp. 55-65
Author(s):  
Nikita Jain ◽  
Ayush Chauhan ◽  
Prakhar Tripathi ◽  
Saad Bin Moosa ◽  
Prateek Aggarwal ◽  
...  
Keyword(s):  
Image Analysis ◽  
Convolutional Network ◽  
Cell Image ◽  
Cell Image Analysis ◽  
Deep Convolutional Network

Use of Computerized Cell Image Analysis to Characterize Cell Nucleus Populations from Normal and Neoplastic Renal Tissues

1991 ◽  
Vol 19 (2) ◽  
pp. 155-164 ◽  
Author(s):  
Kaat vanden Houte ◽  
Robert Kiss ◽  
Carine de Prez ◽  
Alain Verhest ◽  
Jean-Lambert Pasteels ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cell Nucleus ◽  
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
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