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Related Experiment Video

Updated: Mar 22, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Decoupling Target Semantics via Text-Anchored Visual Contrast for Semi-Supervised Medical Image Segmentation.

Qingjie Zeng, Huan Luo, Xinke Ma

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 20, 2026
    PubMed
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    This study introduces Text-anchored Visual Decoupling (TeViD), a new framework for semi-supervised medical image segmentation. TeViD improves accuracy by disentangling target and background information using both visual and textual data.

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Semi-supervised learning (SSL) reduces annotation needs but struggles with ambiguity under limited supervision.
    • Existing text-enhanced SSL methods often prioritize feature fusion over crucial target semantics for segmentation.
    • Medical image segmentation requires accurate differentiation of target structures from background, especially with scarce labeled data.

    Purpose of the Study:

    • To propose a novel Text-anchored Visual Decoupling (TeViD) framework for semi-supervised medical image segmentation.
    • To address semantic ambiguity and improve segmentation performance by leveraging both visual and textual information.
    • To enhance the disentanglement of target and background representations in medical images.

    Main Methods:

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    Last Updated: Mar 22, 2026

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    • A teacher-student architecture with a dual-decoder design to disentangle target and background representations.
    • A reversed cross-supervision mechanism for unlabeled data to improve decoder diversity and semantic separation.
    • Two contrastive learning objectives: teacher-guided visual contrastive loss and text-anchored contrastive loss for semantic reinforcement.

    Main Results:

    • TeViD consistently outperformed standard SSL and text-enhanced SSL methods across five diverse medical imaging datasets (X-ray, pathology, ultrasound, MRI, CT).
    • Achieved average improvements of 5.72% in Dice score and 8.15% in mean Intersection over Union (mIoU) compared to the second-best method.
    • Demonstrated effective semantic disentanglement from both visual and textual perspectives, enhancing segmentation accuracy.

    Conclusions:

    • The proposed TeViD framework offers a significant advancement in semi-supervised medical image segmentation.
    • Leveraging text-anchored visual decoupling effectively addresses semantic ambiguity and improves segmentation performance.
    • TeViD provides a robust and versatile solution for various medical imaging modalities.