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

Laser-Based Micro/Nano Additive Manufacturing of Conductive Structures on Transparent Substrates: Technical Approaches, Challenges, and Future Prospects.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.

Pattern recognition·2025
Same author

A deep semi-supervised learning approach to the detection of glaucoma on out-of-distribution retinal fundus image datasets.

BMC ophthalmology·2025
Same author

Adaptive boundary-enhanced Dice loss for image segmentation.

Biomedical signal processing and control·2025
Same author

Parameter Optimization for Laser Peen Forming on 6005A-T6 Aluminum Alloy Plates to Enhance the Constrained Deformation of Integral Stiffened Plates.

Materials (Basel, Switzerland)·2024
Same author

Semi-supervised image segmentation using a residual-driven mean teacher and an exponential Dice loss.

Artificial intelligence in medicine·2024

Related Experiment Video

Updated: Jun 28, 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

2.7K

UGLS: an uncertainty guided deep learning strategy for accurate image segmentation.

Xiaoguo Yang1, Yanyan Zheng1, Chenyang Mei2

  • 1Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, Wenzhou, China.

Frontiers in Physiology
|April 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an uncertainty-guided deep learning strategy (UGLS) to improve image segmentation accuracy. The novel method enhances U-Net performance for segmenting optic cup and lung regions in medical images.

Keywords:
deep learningfundus imageimage segmentationoptic cupoptic cup deep learningtraining strategy

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

8.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K

Related Experiment Videos

Last Updated: Jun 28, 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

2.7K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

8.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K

Area of Science:

  • Computer Vision
  • Medical Image Analysis
  • Deep Learning

Background:

  • Accurate image segmentation is vital for computer vision and medical diagnostics.
  • Existing neural networks like U-Net require enhancement for precise multi-object segmentation across diverse image modalities.

Purpose of the Study:

  • To develop and validate a novel uncertainty-guided deep learning strategy (UGLS) for improved image segmentation.
  • To enhance the performance of the U-Net architecture in segmenting multiple objects of interest.

Main Methods:

  • Developed a novel uncertainty-guided deep learning strategy (UGLS).
  • Introduced a boundary uncertainty map based on coarse segmentation from U-Net.
  • Combined uncertainty maps with input images for fine object segmentation.

Main Results:

  • Achieved an average Dice Score (DS) of 0.8791 and sensitivity (SEN) of 0.8858 for optic cup segmentation.
  • Obtained high Dice Scores (0.9605-0.9668) for left and right lung segmentation from X-ray images.
  • Demonstrated superior or comparable performance against five advanced segmentation networks.

Conclusions:

  • The UGLS significantly improves U-Net's segmentation performance.
  • The method is effective for segmenting optic cup regions in fundus images and lung regions in X-ray images.
  • UGLS offers a promising approach for enhancing medical image segmentation accuracy.