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

Assessment of Abdominal Adiposity in OSA Using Combined PET Imaging and MRI: Impact of Short-Term CPAP.

Chest·2026
Same author

Label-Free Holographic Imaging Flow Cytometry With Deep-Learning-Based Detection and Classification of Thousands of Cells Per Second.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
Same author

A Pre-Trained Model Customization Framework for Accelerated PET/MR Segmentation of Abdominal Fat in Obstructive Sleep Apnea.

Diagnostics (Basel, Switzerland)·2025
Same author

ProtoSAM for automated one shot medical image segmentation using foundational models.

Scientific reports·2025
Same author

Label-free refractive index mapping of human sperm cells.

Biomedical optics express·2025
Same author

Rare cell classification using label-free imaging flow cytometry <i>via</i> motion-sensitive-triggered interferometry.

Lab on a chip·2025

Related Experiment Video

Updated: Oct 9, 2025

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells
12:48

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

Published on: February 16, 2018

7.5K

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations.

Shani Ben Baruch1, Noa Rotman-Nativ1, Alon Baram1

  • 1Department of Biomedical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.

Cells
|December 24, 2021
PubMed
Summary

This study introduces a novel deep-learning method for classifying cancer cells using optical imaging. The approach integrates cell morphology and dynamic fluctuations, improving accuracy in distinguishing cells with different metastatic potentials.

Keywords:
cancer cellsclassificationdeep learningfluctuations

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.7K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

518

Related Experiment Videos

Last Updated: Oct 9, 2025

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells
12:48

A Time-lapse, Label-free, Quantitative Phase Imaging Study of Dormant and Active Human Cancer Cells

Published on: February 16, 2018

7.5K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.7K
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

518

Area of Science:

  • Biophysics
  • Cell Biology
  • Medical Imaging

Background:

  • Cancer cell morphology and mechanical properties change with disease progression.
  • Quantitative Phase Imaging (QPI) provides label-free cell analysis.
  • Deep learning frameworks can analyze complex biological data.

Purpose of the Study:

  • To develop and validate a new deep-learning classification approach for live cells.
  • To integrate spatial-temporal fluctuations and optical thickness for improved cell classification.
  • To differentiate cancer cell lines with varying metastatic potential.

Main Methods:

  • Utilized common-path quantitative-phase dynamic imaging to acquire cell data.
  • Processed images using a deep-learning framework with a triple-path architecture.
  • Integrated quantitative optical thickness maps with spatio-temporal fluctuation maps.

Main Results:

  • The integrated triple-path deep-learning architecture outperformed classification based solely on morphology.
  • Demonstrated improved classification accuracy by incorporating cell dynamics (fluctuations).
  • Successfully classified cancer cell lines of different metastatic potentials from the same patient.

Conclusions:

  • Integrating spatio-temporal cell fluctuations with morphological data enhances deep-learning classification accuracy.
  • This approach offers a powerful tool for analyzing cell dynamics and metastatic potential.
  • The method shows promise for advancing cancer research and diagnostics.