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Related Experiment Video

Updated: May 15, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Triple collaborative consistency with Mamba for semi-supervised 3D medical image segmentation.

Yufei Gao1, Bingning Liu1, Qing Li2

  • 1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, China.

Medical Physics
|May 14, 2026
PubMed
Summary

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This study introduces the Tri-branch Collaborative Consistency model with Mamba (TCC-Mamba) for improved semi-supervised medical image segmentation. The TCC-Mamba enhances accuracy in complex regions by effectively modeling long-range dependencies and utilizing geometric information.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Current semi-supervised segmentation methods struggle with cross-branch collaboration and inefficient long-range modeling for complex anatomical structures.
  • Limitations include suboptimal boundary segmentation and difficulties balancing accuracy and efficiency in high-resolution medical images.

Purpose of the Study:

  • To introduce the Tri-branch Collaborative Consistency model based on Mamba long-range modeling (TCC-Mamba).
  • To reduce annotation dependency and improve segmentation accuracy in complex medical image regions.

Main Methods:

  • A shared encoder and tri-branch decoder architecture with a closed-loop cross-pseudo-label supervision mechanism for collaborative optimization.
  • Integration of a geometric consistency loss function to improve boundary awareness.
Keywords:
3D medical image segmentationconsistency lossmambapseudo‐labelssemi‐supervised learning

Related Experiment Videos

Last Updated: May 15, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

  • Utilizing the SpatialTriMamba module for efficient long-range dependency modeling and dynamic fusion of global context and local features.

    • Main Results:

    • Experiments on Left Atrium, Pancreas CT, and ACDC datasets with limited labeled data (10-30%) demonstrated superior performance.
    • The TCC-Mamba outperformed six advanced semi-supervised methods in segmentation accuracy.

    Conclusions:

    • TCC-Mamba offers novel methodologies for medical image segmentation, effectively capturing long-range features and geometric information via signed distance maps.
    • The model provides an efficient and reliable solution for semi-supervised segmentation of complex anatomical structures.