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Updated: Aug 30, 2025

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis.

Furkan Eren, Mete Aslan, Dilek Kanarya

IEEE Journal of Biomedical and Health Informatics

|September 2, 2022

Summary

This summary is machine-generated.

DeepCAN, a novel deep learning system, automates cell counting and viability analysis, offering a precise and efficient alternative to traditional methods. This advanced image cytometry tool ensures accurate results across various cell types.

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Last Updated: Aug 30, 2025

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

Viability Assays for Cells in Culture

Viability Assays for Cells in Culture

Published on: January 20, 2014

Area of Science:

Biotechnology
Computational Biology
Cell Biology

Background:

Traditional image cytometry using hemocytometers is subjective and error-prone.
Deep learning offers a powerful, accurate, and efficient alternative for image cytometry.
Existing methods lack comprehensive solutions for automated cell counting and viability analysis.

Purpose of the Study:

To develop and validate DeepCAN, a modular deep learning system for automated cell counting and viability analysis.
To demonstrate DeepCAN's effectiveness as a viable alternative to flow cytometry and Coulter counting.
To improve the precision and efficiency of cytometric feature monitoring in life science applications.

Main Methods:

Developed DeepCAN, a modular system with three neural network blocks: Parallel Segmenter (modified U-Net), Cluster CNN (LeNet-5), and Viability CNN (Opto-Net).
Trained models on diverse cell image datasets (A2780, yeasts) for segmentation, cluster separation, and viability classification.
Validated segmentation performance using Precision/Recall/F1-Scores and overall system accuracy on multiple cell lines (A2780, A549, Colo, MDA-MB-231).

Main Results:

Achieved high segmentation performance with Precision/Recall/F1-Scores of 96.52%/96.45%/98.06% for the Parallel Segmenter and Cluster CNN combination.
Demonstrated high cell counting and viability analysis accuracies across multiple cell lines, with results ranging from 85.32% to 99.02%.
DeepCAN provides a complete, automated solution for image cytometry, outperforming traditional methods.

Conclusions:

DeepCAN offers a robust and accurate deep learning solution for automated cell counting and viability analysis.
The modular design allows for adaptability and potential application to various cell types and cytometric analyses.
Deep learning-based image cytometry presents a simpler, more affordable, and highly accurate alternative to conventional techniques.