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 Experiment Videos

A Multimodal Dense Parallel Global Attention Mechanism for Brain Tumor Image Segmentation.

Zhuye Xu1, Ru Qiao1

  • 1School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Journal of Imaging
|June 25, 2026
PubMed
Summary

This study introduces a 3D deep learning network for automatic brain tumor segmentation using multimodal MRI data. The model effectively fuses features to improve segmentation accuracy for whole tumors, tumor cores, and enhanced tumors.

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

Enhancing cold resistance in Banana (Musa spp.) through EMS-induced mutagenesis, L-Hyp pressure selection: phenotypic alterations, biomass composition, and transcriptomic insights.

BMC plant biology·2024
Same author

Identification of transcription factors interacting with a 1274 bp promoter of MaPIP1;1 which confers high-level gene expression and drought stress Inducibility in transgenic Arabidopsis thaliana.

BMC plant biology·2020
See all related articles

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Brain tumor segmentation from 3D MRI is challenging due to lesion characteristics.
  • Existing methods struggle with small sizes, ambiguous boundaries, and varied morphology.

Purpose of the Study:

  • To develop a fully automatic 3D deep learning network for accurate brain tumor segmentation.
  • To integrate morphological and anatomical information using a multi-task learning framework.

Main Methods:

  • Proposed a multimodal feature fusion module to adaptively weight features from T1, T1ce, T2, and FLAIR MRI modalities.
  • Introduced a ConvReXt downsampling module to preserve fine-grained semantic details.
  • Developed a dense parallel global attention module for capturing local and long-range dependencies.
Keywords:
brain tumorfeature recombinationimage segmentationmultimodal

Related Experiment Videos

Main Results:

  • Achieved average Dice coefficients of 92.54% (whole tumor), 89.21% (tumor core), and 86.54% (enhanced tumor) on the BraTS2020 dataset.
  • Demonstrated competitive performance against state-of-the-art methods like nnFormer.

Conclusions:

  • The proposed model effectively fuses multimodal and multi-scale features for improved brain tumor segmentation.
  • The network offers a robust solution for automatic segmentation of brain tumors in 3D MRI.