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

3D Medical Image Segmentation with 3D Modelling.

Bioengineering (Basel, Switzerland)·2026
Same author

Segmentation and Multimodal Characterization of Metal Particles in the Human Hippocampus Using Discrete Segmentation Algorithms and Correlation Spectral Analysis.

Molecules (Basel, Switzerland)·2026
Same author

A Systematic Review of Evidence, Misinterpretations, and the Urgent Need for Population-Specific Reference Standards Related to Vitamin D Deficiency in India: A Global Myth Imposed Locally?

Cureus·2026
Same author

Development of Magnetic Sponges Using Steel Melting on 3D Carbonized Spongin Scaffolds Under Extreme Biomimetics Conditions.

Biomimetics (Basel, Switzerland)·2025
Same author

The sample size matters: evaluating minimum and reasonable values in prevalence studies.

International journal for parasitology·2025
Same author

Computational study of endogenous magnetic particles' effect on action potential processing in a Purkinje cell model.

Bratislavske lekarske listy·2024

Related Experiment Video

Updated: Jul 18, 2025

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

2.8K

Traditional and deep learning-oriented medical and biological image analysis.

Maria Zdimalova, Mridul Ghosh, Asifuzzaman Lasker

    Bratislavske Lekarske Listy
    |August 28, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances medical and biological image segmentation using advanced techniques like GrabCut, fuzzy logic, and deep learning (U-Net). These methods improve diagnostic accuracy and cell analysis by providing sharper image boundaries.

    More Related Videos

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.8K
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    795

    Related Experiment Videos

    Last Updated: Jul 18, 2025

    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

    2.8K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.8K
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    795

    Area of Science:

    • Medical Imaging
    • Bioinformatics
    • Computational Biology

    Background:

    • Accurate segmentation of medical and biological images is crucial for diagnostics and research.
    • Existing methods face challenges in segmenting complex cellular structures and medical data.

    Purpose of the Study:

    • To investigate and improve image segmentation techniques for medical and biological data.
    • To enhance diagnostic processes and cell/iron diagnostics through advanced image analysis.
    • To introduce novel software and mathematical approaches for superior segmentation results.

    Main Methods:

    • Implementation of the GrabCut algorithm using C++.
    • Development of a fuzzy approach and fuzzy processing for tissue analysis in Matlab.
    • Application of deep learning with a U-Net architecture for brain cell parameter measurement.

    Main Results:

    • Improved segmentation of biological and medical data, yielding better object boundaries and sharper edges.
    • Successful processing of data previously intractable with other methods.
    • Demonstrated potential for enhanced diagnostic and cellular analysis.

    Conclusions:

    • The proposed methods, including GrabCut, fuzzy logic, and deep learning, significantly advance image segmentation in medical and biological fields.
    • These techniques offer improved accuracy and detail for diagnostic and research applications.
    • Further extension to other medical and biological domains is promising.