Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Potential Anticancer Effects of Limonene Associated with Reactive Oxygen Species Generation, Apoptosis Induction, and NF-κB Modulation in Papillary Renal Cell Carcinoma: A Preliminary Study.

Current developments in nutrition·2026
Same author

Citalopram enhances cisplatin-induced cytotoxicity in T24 bladder cancer cells: An in vitro study.

Experimental and molecular pathology·2026
Same author

Metformin and Flutamide Combination Therapy's Efficacy and Safety in Prostate Cancer Cell Lines.

Prostate cancer·2026
Same author

Advancing Colorimetric Analysis in Enzyme-Linked Immunosorbent Assays: Harnessing Nonlinear Regression for Improved Accuracy and Predictive Performance.

ACS omega·2026
Same author

Dietary Trends and Projections Among Iranian Adults: Analysis of the Iran-WHO STEPS Survey (2005-2021) and Modelled Estimates to 2025.

Journal of human nutrition and dietetics : the official journal of the British Dietetic Association·2026
Same author

Melatonin enhances the antitumor and immunomodulatory effects of Bacillus Calmette-Guérin immunotherapy in a murine bladder cancer model.

Molecular biology reports·2026

Related Experiment Video

Updated: May 6, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

1.5K

Automated counting of prostate cell types with image processing and machine learning.

Babak Kamali Doust Azad1, Aryan Norouzzadeh Hakimi2, Seyed Mohammad Tabatabaei1

  • 1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.

Plos One
|May 4, 2026
PubMed
Summary

This study introduces a new automated system for counting prostate cancer cells using mobile phone images and deep learning. The method offers a more accurate and efficient alternative to manual cell counting in research and diagnostics.

More Related Videos

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

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

Published on: February 15, 2022

2.0K
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.4K

Related Experiment Videos

Last Updated: May 6, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

1.5K
Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

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

Published on: February 15, 2022

2.0K
Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

Published on: April 8, 2016

12.4K

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Oncology

Background:

  • Traditional hemocytometry for cell counting is manual, time-consuming, and prone to errors.
  • Limitations in precision and throughput of manual methods impede prostate cancer research and diagnostics.

Purpose of the Study:

  • To develop a novel automated software system for accurate prostate cancer cell enumeration.
  • To leverage machine learning and mobile phone imaging for accessible cell counting.

Main Methods:

  • Utilized a convolutional neural network (CNN) and selective search for region identification.
  • Integrated image processing with deep learning for cell detection and quantification.
  • Developed a two-stage pipeline to handle variability in mobile-captured images.

Main Results:

  • The automated system demonstrated superior accuracy compared to manual counting methods.
  • The approach effectively identified and quantified prostate cancer cells from mobile phone images.
  • The system robustly handled image variability and extraneous content.

Conclusions:

  • The proposed automated framework provides a practical and scalable solution for cell counting.
  • This technology can significantly enhance the reliability and efficiency of prostate cancer cell enumeration in research and clinical settings.
  • Mobile phone-based automated cell counting offers a promising avenue for accessible diagnostics.