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

Batten disease: features to facilitate early diagnosis.

The British journal of ophthalmology·2006
Same author

Identifying breast cancer patients at risk for Central Nervous System (CNS) metastases in trials of the International Breast Cancer Study Group (IBCSG).

Annals of oncology : official journal of the European Society for Medical Oncology·2006
Same author

The seasonal fertility of North American bison (Bison bison) bulls.

Animal reproduction science·2006
Same author

Genome scan of schizophrenia families in a large Veterans Affairs Cooperative Study sample: evidence for linkage to 18p11.32 and for racial heterogeneity on chromosomes 6 and 14.

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics·2005
Same author

Are endogenous LH levels during ovarian stimulation for IVF using GnRH analogues associated with the probability of ongoing pregnancy? A systematic review.

Human reproduction update·2005
Same author

Quality of care for women presenting with benign breast conditions.

Internal medicine journal·2005
Same journal

CytoScan: Automated Detection of Technical Anomalies for Cytometry Quality Control.

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

The 1st Mediterranean Meeting on Flow Cytometry: Forging New Collaborations Across the Mediterranean and Beyond.

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

Publication Guidelines for Optimized Multiparameter Immunolabeling Panels (OMIPs).

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

A Modular High-Parameter Flow Cytometry Framework: Pre-Analytical Optimization and Validation for Clinical Research.

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

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

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

Comparison of Tissue Preparations to Identify and Phenotype T Cells in Human Colorectal Tumor Tissue.

Cytometry. Part A : the journal of the International Society for Analytical Cytology·2026
See all related articles

Related Experiment Video

Updated: Jul 6, 2026

A Simple, Quick, and Partially Automated Protocol for the Isolation of Single Nuclei from Frozen Mammalian Tissues for Single Nucleus Sequencing
07:12

A Simple, Quick, and Partially Automated Protocol for the Isolation of Single Nuclei from Frozen Mammalian Tissues for Single Nucleus Sequencing

Published on: July 28, 2023

A high-throughput system for segmenting nuclei using multiscale techniques.

Prabhakar R Gudla1, K Nandy, J Collins

  • 1Image Analysis Laboratory, Advanced Technology Program, SAIC-Frederick, NCI-Frederick, Frederick, Maryland 21702, USA. reddyg@ncifcrf.gov

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|March 15, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an automated algorithm for precise cell nuclei segmentation in fluorescence images, crucial for spatial gene analysis and cancer research. The method accurately identifies individual and clustered nuclei, improving reproducibility in high-throughput cytometry.

More Related Videos

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

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Related Experiment Videos

Last Updated: Jul 6, 2026

A Simple, Quick, and Partially Automated Protocol for the Isolation of Single Nuclei from Frozen Mammalian Tissues for Single Nucleus Sequencing
07:12

A Simple, Quick, and Partially Automated Protocol for the Isolation of Single Nuclei from Frozen Mammalian Tissues for Single Nucleus Sequencing

Published on: July 28, 2023

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

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Area of Science:

  • Cell biology
  • Biomedical imaging
  • Computational pathology

Background:

  • Manual cell nuclei segmentation is time-consuming and inconsistent.
  • Existing automatic methods struggle with uneven lighting and clustered nuclei.
  • Accurate nuclei segmentation is vital for spatial gene analysis and cancer research.

Purpose of the Study:

  • To develop a robust, automated algorithm for accurate cell nuclei segmentation.
  • To overcome limitations of current methods in handling image variations and nucleus clustering.
  • To enable precise spatial analysis of DNA sequences using fluorescence in situ hybridization (FISH).

Main Methods:

  • A modular, model-based algorithm employing multiscale edge reconstruction and entropy-based thresholding.
  • Nuclei oversegmentation followed by area-based merging and multistage classification.
  • Automatic parameter estimation and classifier training for complete automation.

Main Results:

  • The algorithm achieved high accuracy in segmenting individual nuclei (99.8% +/- 0.3%) and nuclei within clusters (95.5% +/- 5.1%).
  • Segmented nuclei boundaries showed high accuracy compared to manual segmentation (0.26 microm RMS deviation).
  • The method demonstrated robustness against background nonuniformity and nucleus clustering.

Conclusions:

  • The proposed algorithm offers an efficient, accurate, and automated solution for cell nuclei segmentation.
  • It enhances reproducibility and reduces bias in spatial DNA sequence analysis.
  • This tool is valuable for high-throughput cytometry and cancer research applications.