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

AI-Powered Resting 12-Lead Electrocardiogram Algorithm for Predicting Low Peak Oxygen Consumption: Development and Validation Study.

JMIR medical informatics·2026
Same author

Exciplex-Forming Co-Host Systems for Efficient TADF and Phosphorescent Organic Light-Emitting Diodes.

Chemistry, an Asian journal·2026
Same author

Topical 5-aminolevulinic acid photodynamic therapy-induced purpura: Involvement of dual antiplatelet therapy (DAPT).

Photodiagnosis and photodynamic therapy·2025
Same author

Carbon fiber-laminated epoxy resin causing chronic occupational dermatosis: presentation with erythroderma and dyschromia-a case report.

Journal of occupational health·2025
Same author

Comparative Study of Continuous-Flow Reactors for Emulsion Polymerization.

Polymers·2025
Same author

Camera-Based Photoplethysmography for Measuring Heartbeat Intervals During General Anesthesia.

Anesthesia and analgesia·2025

Related Experiment Video

Updated: Dec 19, 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

3.2K

Lymphatic vessel segmentation in optical coherence tomography by adding U-Net-based CNN for artifact minimization.

Pei-Yu Lai1, Chung-Hsing Chang2,3, Hong-Ren Su4

  • 1Department of Biophotonics, National Yang-Ming University, 155, Sec-2, Li-Nong Street, Taipei 112, Taiwan.

Biomedical Optics Express
|June 6, 2020
PubMed
Summary

A new method accurately segments lymphatic capillaries using optical coherence tomography (OCT) and AI. This advance improves imaging of the lymphatic system for better immune response studies.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.4K
Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse
09:52

Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse

Published on: March 8, 2013

16.7K

Related Experiment Videos

Last Updated: Dec 19, 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

3.2K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.4K
Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse
09:52

Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse

Published on: March 8, 2013

16.7K

Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography
  • Lymphatic System Research

Background:

  • The lymphatic system is crucial for fluid transport and immune response.
  • Noninvasive imaging of lymphatic capillaries is challenging.
  • Optical coherence tomography (OCT) offers label-free imaging potential.

Purpose of the Study:

  • To develop and validate an advanced method for segmenting lymphatic capillaries using OCT.
  • To improve the precision and reduce artifacts in lymphatic vessel imaging.

Main Methods:

  • Combined U-Net-based convolutional neural network (CNN) with a Hessian vesselness filter.
  • Utilized modified intensity-thresholding based on a binarized Hessian mask.
  • Employed OCT for noninvasive, label-free imaging of lymphatic capillaries.

Main Results:

  • The proposed method achieved high segmentation accuracy.
  • Achieved a Dice coefficient of 0.83, precision of 0.859, and recall of 0.803.
  • Demonstrated more precise shape extraction with minimal artifacts compared to previous methods.

Conclusions:

  • The novel segmentation technique enhances OCT imaging of lymphatic capillaries.
  • This method offers a significant improvement for studying lymphatic system function.
  • The approach shows promise for future biomedical imaging applications.