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

Perioperative NLR/PLR dynamic phenotypes and prediction of postoperative recurrence in locally advanced gastric cancer: a retrospective cohort study.

BMC cancer·2026
Same author

Coordination chemistry-enabled drug delivery systems: metal-ligand platforms for controlled release and targeted therapeutics.

Journal of nanobiotechnology·2026
Same author

Orlistat targets NEDD8 conjugating enzyme UBC12 for cancer therapy.

Pharmacological research·2026
Same author

Associations of eight insulin resistance-related indices and genetic risk with incident cardiometabolic multimorbidity among participants with hypertension: a large prospective cohort study.

Cardiovascular diabetology·2026
Same author

Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.

Materials today. Bio·2026
Same author

Nanozyme for precision treatment of hepatocellular carcinoma.

Materials today. Bio·2026

Related Experiment Video

Updated: Nov 4, 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

3.1K

Lung computed tomography image segmentation based on U-Net network fused with dilated convolution.

Kuan-Bing Chen1, Ying Xuan2, Ai-Jun Lin3

  • 1Department of Thoracic Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.

Computer Methods and Programs in Biomedicine
|May 31, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces DC-U-Net, an improved U-Net model for lung CT image segmentation. It achieves superior accuracy and efficiency in segmenting lung tissues, aiding lung cancer treatment.

Keywords:
Dilated convolutionGround truthImage segmentationLung CTU-Net network

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

9.8K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.8K

Related Experiment Videos

Last Updated: Nov 4, 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

3.1K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.8K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.8K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer-Aided Diagnosis

Background:

  • Accurate lung segmentation in CT images is crucial for effective lung cancer treatment.
  • Existing segmentation methods often lack precision and efficiency.

Purpose of the Study:

  • To develop an accurate and efficient method for lung CT image segmentation.
  • To improve the overall efficiency of lung cancer treatment through enhanced medical image analysis.

Main Methods:

  • A novel DC-U-Net model integrating dilated convolution was proposed.
  • Performance was evaluated against Otsu and region growing algorithms.
  • Segmentation accuracy was quantified using Intersection over Union (IOU), Dice coefficient, Precision, and Recall.

Main Results:

  • DC-U-Net demonstrated superior segmentation accuracy compared to Otsu and region growing.
  • DC-U-Net achieved an IOU of 0.9627 and a Dice coefficient of 0.9743.
  • The segmented images produced by DC-U-Net closely matched the Ground truth.

Conclusions:

  • The DC-U-Net model enables automatic and accurate segmentation of original lung CT images.
  • This approach simplifies medical image segmentation steps and enhances speed.
  • The model provides improved segmentation for subsequent analysis of lung vasculature and airways.