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

The role of miRNA in modulating oxidative stress in preeclampsia: current knowledge.

RNA biology·2026
Same author

Biosynthetic Gene Cluster Diversity and Species-Specific Metabolic Potential in Ustilaginaceae.

Journal of fungi (Basel, Switzerland)·2026
Same author

USP4 modulates ZBP1 ubiquitination to regulate microglial PANoptosis and functional outcomes following traumatic brain injury.

Cell death and differentiation·2026
Same author

Targeting TREM2<sup>+</sup>tumor-associated macrophages reactivates antitumor immunity and improves anti-PD-1 efficacy in gastric cancer.

Seminars in oncology·2026
Same author

Lethal clinical outcome and immune desert contexture in refractory gastric cancer harboring CCNE1 amplification and overexpression.

British journal of cancer·2026
Same author

CD74<sup>+</sup>tumour-Associated Macrophages Promote Immune Escape through Autonomous PD-L1 Eexpression in Gastric Cancer.

Journal of gastrointestinal cancer·2026

Related Experiment Video

Updated: Aug 23, 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

2.9K

Multi-scale contextual semantic enhancement network for 3D medical image segmentation.

Tingjian Xia1, Guoheng Huang1, Chi-Man Pun2

  • 1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, People's Republic of China.

Physics in Medicine and Biology
|November 1, 2022
PubMed
Summary

This study introduces a new 3D MCSE-Net for accurate medical image segmentation, improving tumor detection by addressing scale variations, blurred boundaries, and class imbalance for better diagnosis and treatment planning.

Keywords:
3D medical image segmentationclass imbalancefeature enhancementliver tumormulti-scale contextnasopharyngeal carcinoma

More Related Videos

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

2.6K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

483

Related Experiment Videos

Last Updated: Aug 23, 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

2.9K
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

2.6K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

483

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Computational pathology

Background:

  • Accurate medical image segmentation is vital for disease diagnosis and treatment planning.
  • Existing convolutional neural network methods struggle with lesion scale variations, blurred boundaries, and class imbalance.

Purpose of the Study:

  • To develop a novel segmentation framework, the 3D multi-scale contextual semantic enhancement network (3D MCSE-Net), to overcome current limitations in medical image segmentation.
  • To enhance the accuracy and efficiency of tumor segmentation in medical imaging.

Main Methods:

  • The 3D MCSE-Net incorporates a multi-scale context pyramid fusion module (MCPFM) to handle scale variations.
  • A triple feature adaptive enhancement module (TFAEM) is used to refine lesion boundaries.
  • An asymmetric class correction loss (ACCL) function addresses class imbalance issues.

Main Results:

  • The 3D MCSE-Net demonstrated superior performance on nasopharyngeal cancer tumor segmentation (NPCTS), liver tumor segmentation (LiTS), and 3Dircadb datasets.
  • Experimental results confirmed the effectiveness and generalizability of the proposed modules and the overall framework.
  • The integrated components showed mutually reinforcing properties, leading to improved segmentation accuracy.

Conclusions:

  • The 3D MCSE-Net effectively addresses challenges in medical image segmentation, including scale variation, blurred boundaries, and class imbalance.
  • The proposed method significantly improves tumor segmentation accuracy, aiding clinical diagnosis and treatment planning.
  • This framework offers a promising advancement for automated medical image analysis.