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

Correction: Ruan et al. Comparison of Extraction, Isolation, Purification, Structural Characterization and Immunomodulatory Activity of Polysaccharides from Two Species of <i>Cistanche</i>. <i>Molecules</i> 2025, <i>30</i>, 4754.

Molecules (Basel, Switzerland)·2026
Same author

WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Reconstructing shared visual experiences from human brain activity across individuals.

Medical image analysis·2026
Same author

Data and knowledge-driven imaging biomarkers for lumbar aging and degenerative risk stratification monitoring.

NPJ digital medicine·2026
Same author

A Single Reference-Guided Adaptation of Foundation Model Predictions for High-Performance Image Segmentation.

IEEE transactions on bio-medical engineering·2026
Same author

MRI-based interpretable deep learning radiomics for predicting treatment response in axial spondyloarthritis.

European journal of radiology·2026

Related Experiment Video

Updated: Jul 4, 2026

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

WAS-Mamba: 3D Medical Image Segmentation via Windowed Attention State Space Model.

Xueren Zhang, Xianghong Wang, Nuo Tong

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 12, 2026
    PubMed
    Summary

    WAS-Mamba improves medical image segmentation by enhancing the Mamba state space model (SSM). This novel approach better captures both local and global features, leading to more accurate results with reduced computational cost.

    More Related Videos

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    Related Experiment Videos

    Last Updated: Jul 4, 2026

    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

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    Area of Science:

    • Medical imaging
    • Computer vision
    • Deep learning

    Background:

    • Mamba (SSM) excels at long-range dependencies in medical image segmentation but struggles with simultaneous local and global feature capture.
    • Existing methods exhibit an imbalance in local vs. global modeling, impacting segmentation performance.

    Purpose of the Study:

    • To introduce WAS-Mamba, a novel method for Mamba-based medical image segmentation.
    • To enhance the capture of both local image features and global context for improved segmentation accuracy.

    Main Methods:

    • Proposed WAS-Mamba incorporating a cross-channel window scanning strategy (CCWScan) to preserve local image features.
    • Implemented a weighted state space model (WSSM) for dynamic fusion of spatial and frequency domain information.

    Main Results:

    • Validated WAS-Mamba on five diverse datasets (CT/MRI): Synapse, BTCV, ACDC, BraTS, Decathlon-Lung.
    • Achieved a Dice coefficient of 88.09% on the Synapse dataset.
    • Reduced computational complexity and inference time by 33% compared to the second-best model.

    Conclusions:

    • WAS-Mamba demonstrates superior performance in medical image segmentation compared to existing methods.
    • The method effectively balances local detail and global context modeling.
    • WAS-Mamba offers significant improvements in accuracy and efficiency for medical image segmentation tasks.