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

The prognostic implications of autoimmune hemolytic anemia in chronic lymphocytic leukemia across treatment eras.

Haematologica·2026
Same author

Extracellular Vesicles for Therapeutic Applications: A Translational Framework Integrating Sources, Administration Routes, Indications, Quality Control, and Regulatory Systems.

International journal of nanomedicine·2026
Same author

Disease characteristics, treatment, and outcomes in Chinese chronic lymphocytic leukemia patients following BTK inhibitor discontinuation: a multicenter real-world study.

Frontiers in medicine·2026
Same author

Single-Cell Profiling Identifies SLC2A5-Mediated Fructose Metabolism as a Vulnerability in Primary CNS Lymphoma.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

MYD88 variants in chronic lymphocytic leukemia exhibit distinct biological behaviors and prognostic implications.

Leukemia·2026
Same author

Talquetamab-Daratumumab in Relapsed or Refractory Myeloma.

The New England journal of medicine·2026

Related Experiment Video

Updated: Jan 8, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

12.0K

Enhanced stem cell image segmentation by leveraging visual processing mechanisms.

Zheng-Mian Zhang1, Hai-Jun Wang2,3, Xiao Liang3,4

  • 1Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian, China.

Frontiers in Bioengineering and Biotechnology
|December 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced stem cell image segmentation method. It significantly improves accuracy and reduces errors for better stem cell analysis.

Keywords:
confluencyimage segmentationphase contrast microscopestem cell image processingvisual information cognitive mechanism

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.2K

Related Experiment Videos

Last Updated: Jan 8, 2026

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

12.0K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

10.2K

Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Cell Biology

Background:

  • Conventional stem cell (SC) image segmentation methods have limitations.
  • Analysis of cognitive principles in visual information processing is key.
  • Phase-contrast microscopy images of stem cells were used to evaluate existing methods.

Purpose of the Study:

  • To apply visual information processing mechanisms for stem cell image segmentation.
  • To develop an optimized segmentation method addressing limitations of traditional approaches.
  • To enhance the efficacy of stem cell image segmentation.

Main Methods:

  • Developed an optimized segmentation method incorporating halo correction.
  • Experimentally validated the performance of the proposed method.
  • Compared the proposed method with existing segmentation techniques.

Main Results:

  • Achieved 96.5% segmentation accuracy, 94.9% recall, 91.4% precision, and 93.9% F1-score.
  • Outperformed existing approaches in key segmentation metrics.
  • Demonstrated low confluency error (0.07 on Human Mesenchymal Stem Cells, 0.05 on C2C12 datasets).

Conclusions:

  • The proposed method offers enhanced efficacy for stem cell image segmentation.
  • The optimized approach provides superior performance compared to equivalent methods.
  • Findings support the application of visual processing principles for improved SC image analysis.