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

Rutin-Doped Hybrid Nanoparticles: Effect of the Molecular Doping Density on <i>T</i><sub>1</sub> Relaxivity and Their Application in Targeted Magnetic Resonance Imaging of Malignant Tumors.

Nano letters·2026
Same author

The Impact of Long-Term Chinese Dialect Use on Mandarin Tone Production and Perception: Evidence from Late Middle-Aged Puxian Min Speakers.

Language and speech·2026
Same author

Application Effectiveness Analysis of Artificial Intelligence-assisted Teaching in Standardized Training for Medical Imaging Residents.

Academic radiology·2026
Same author

Higher-order lattice anharmonicity reshaping non-equilibrium carrier dynamics in wide-phonon-gap semiconductors.

National science review·2026
Same author

Lipid Metabolic Effects Induced by Individual and Combined Exposure to Multiple Food Additives in Human Cells.

Toxics·2026
Same author

Catalytic Waste Valorization Must Move beyond Model Systems.

Environmental science & technology·2026

Related Experiment Video

Updated: Jan 11, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

802

CellApop: A knowledge-guided decoupled distillation framework for label-efficient apoptotic cell segmentation and

Chi Dong1, Xuan Xie2, Shuai Yue2

  • 1School of Intelligent Medicine, China Medical University, Liaoning 110122, PR China.

Computer Methods and Programs in Biomedicine
|November 12, 2025
PubMed
Summary

This study introduces CellApop, a deep learning framework for label-free apoptosis detection in microscopy. It accurately quantifies apoptotic cells, outperforming junior experts and reducing manual labeling needs.

Keywords:
Cell apoptosisKnowledge distillationMicroscopic imagesSegmentation

More Related Videos

Quantification of Efferocytosis by Single-cell Fluorescence Microscopy
06:15

Quantification of Efferocytosis by Single-cell Fluorescence Microscopy

Published on: August 18, 2018

13.4K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.9K

Related Experiment Videos

Last Updated: Jan 11, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

802
Quantification of Efferocytosis by Single-cell Fluorescence Microscopy
06:15

Quantification of Efferocytosis by Single-cell Fluorescence Microscopy

Published on: August 18, 2018

13.4K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.9K

Area of Science:

  • Biotechnology
  • Cell Biology
  • Artificial Intelligence

Background:

  • Conventional apoptosis detection relies on labor-intensive, potentially cytotoxic fluorescence staining.
  • Existing methods are unsuitable for real-time monitoring of cellular processes.

Purpose of the Study:

  • To develop a label-free, dynamic detection framework for apoptotic cells using bright-field microscopy.
  • To create a segmentation-based deep learning (DL) model for efficient and accurate apoptosis identification.

Main Methods:

  • A Knowledge-guided Decoupled Distillation (KDD) framework trained a lightweight DL network (CellApop) on 16,472 bright-field cell images.
  • CellApop incorporated advanced modules for improved segmentation accuracy in challenging conditions.
  • Performance was validated using Dice similarity coefficient, Hausdorff Distance, and Intersection over Union.

Main Results:

  • CellApop achieved high Dice scores (0.843 general, 0.754 apoptotic cells) with reduced complexity and latency.
  • The KDD strategy reduced manual labeling by ~80% on a proprietary dataset.
  • Automated apoptosis rates showed high concordance with ground truth and expert assessments.

Conclusions:

  • CellApop provides accurate, efficient, label-free segmentation of apoptotic cells, negating the need for fluorescence staining.
  • The framework is robust and scalable for automated apoptosis quantification and drug-response assessment.
  • This DL approach offers a promising tool for routine experimental workflows in cell biology.