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

Deep-Learning Solution Providing Molecular Marker Subtyping of Breast Cancer Whole Slide Images: Protocol for a UK Clinical Service Evaluation Study.

JMIR research protocols·2026
Same author

Topology-guided hard example mining for cell detection.

Medical image analysis·2026
Same author

Molecular contrastive learning with graph attention network (MoCL-GAT) for enhanced molecular representation.

BMC bioinformatics·2026
Same author

Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.

NPJ breast cancer·2026
Same author

Agreement Across 10 Artificial Intelligence Models in Assessing Human Epidermal Growth Factor Receptor 2 (HER2) Expression in Breast Cancer Whole-Slide Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc·2026
Same author

H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation.

NPJ digital medicine·2025

Related Experiment Video

Updated: May 16, 2026

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

Smart markers for watershed-based cell segmentation.

Can Fahrettin Koyuncu1, Salim Arslan, Irem Durmaz

  • 1Department of Computer Engineering, Bilkent University, Ankara, Turkey.

Plos One
|November 16, 2012
PubMed
Summary

This study introduces smart markers for improved live cell segmentation in phase contrast microscopy. The novel approach enhances marker identification, boosting the performance of watershed algorithms for biological imaging.

More Related Videos

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

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
14:28

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Published on: July 15, 2020

Related Experiment Videos

Last Updated: May 16, 2026

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

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

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
14:28

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Published on: July 15, 2020

Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Microscopy Imaging

Background:

  • Automated cell imaging is crucial for analyzing biological events.
  • Cell segmentation is a critical, yet ill-posed, first step in image analysis.
  • Domain-specific knowledge is often required for accurate segmentation.

Purpose of the Study:

  • To develop an effective approach for segmenting live cells using phase contrast microscopy.
  • To introduce a novel set of "smart markers" for marker-controlled watershed segmentation.
  • To improve segmentation accuracy by incorporating visual cell characteristics.

Main Methods:

  • Proposed a new approach for live cell segmentation.
  • Introduced "smart markers" for marker-controlled watershed algorithm.
  • Utilized domain-specific knowledge of visual cell characteristics to define markers.

Main Results:

  • Evaluated the approach on 1,954 cells.
  • Demonstrated that the proposed smart markers are effective in identifying superior markers.
  • Showed improved segmentation performance of the marker-controlled watershed algorithm.

Conclusions:

  • The proposed smart markers significantly enhance marker identification for watershed segmentation.
  • This approach effectively improves the segmentation of live cells in phase contrast microscopy.
  • Incorporating domain-specific knowledge via smart markers offers a promising direction for cell image analysis.