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

Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions.

Journal of pediatric orthopedics. Part B·2025
Same author

Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays.

Bone reports·2025
Same author

Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy.

Scientific reports·2025
Same author

Scale Selection and Machine Learning-based Cell Segmentation and Tracking in Time Lapse Microscopy.

Research square·2024
Same author

Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

PET Imaging of Neurofibromatosis Type 1 with a Fluorine-18 Labeled Tryptophan Radiotracer.

Pharmaceuticals (Basel, Switzerland)·2024

Related Experiment Video

Updated: Mar 16, 2026

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

10.1K

Joint level-set and spatio-temporal motion detection for cell segmentation.

Fatima Boukari1, Sokratis Makrogiannis2

  • 1Department of Physics and Engineering, Delaware State Univ., 1200 N. DuPont Hwy, Dover, 19901, DE, USA.

BMC Medical Genomics
|August 12, 2016
PubMed
Summary

This study introduces a novel method for segmenting moving cells in microscopy images, improving accuracy and robustness for cell tracking and analysis. The approach enhances quantification of cellular processes like migration and division.

Keywords:
Cell segmentationDensity estimationLevel setsNonlinear diffusion

More Related Videos

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.9K
Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes
07:13

Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes

Published on: February 13, 2021

2.7K

Related Experiment Videos

Last Updated: Mar 16, 2026

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

10.1K
Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.9K
Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes
07:13

Isolation and Time-Lapse Imaging of Primary Mouse Embryonic Palatal Mesenchyme Cells to Analyze Collective Movement Attributes

Published on: February 13, 2021

2.7K

Area of Science:

  • Biomedical imaging analysis
  • Computational biology
  • Cellular dynamics

Background:

  • Accurate cell segmentation is crucial for quantitative analysis of cellular processes in live-cell imaging.
  • Existing methods face challenges with varying cell densities, image quality, and dynamic events like mitosis.

Purpose of the Study:

  • To develop and validate a robust joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation.
  • To improve the accuracy and reliability of cell segmentation in time-lapse microscopy for downstream quantitative tasks.

Main Methods:

  • Proposed a joint spatio-temporal diffusion and region-based level-set optimization for moving cell segmentation.
  • Employed histogram transformation for intensity standardization and Parzen kernels for edge map computation.
  • Utilized watershed-based segmentation and level-set evolution for refining cell boundaries.

Main Results:

  • Achieved an average Dice similarity coefficient of 89% on diverse fluorescence microscopy datasets.
  • Demonstrated significant improvements in segmentation accuracy compared to established Chan-Vese and nonlinear diffusion methods.
  • Validated against reference masks from the international Cell Tracking Challenge.

Conclusions:

  • The proposed method shows high efficiency and robustness, even with challenging image quality, low signal-to-noise ratio, and mitotic events.
  • Enables reliable quantification of cellular behaviors, including migration, growth, and immune responses.
  • Facilitates advanced research in developmental biology, tumorigenesis, and drug discovery.