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Harnessing Lightweight Transformer With Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation.

Xinyu Liu, Zhen Chen, Wuyang Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 5, 2025
    PubMed
    Summary

    Light-UNETR enhances 3D medical image segmentation by improving model and data efficiency. This lightweight transformer significantly reduces computational costs and data requirements, achieving superior performance with less labeled data.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Transformers excel in 3D medical image segmentation but demand high computational resources and extensive labeled data.
    • Current methods face limitations in efficiency and data requirements, hindering widespread clinical adoption.

    Purpose of the Study:

    • To develop a lightweight transformer (Light-UNETR) for efficient 3D medical image segmentation.
    • To enhance both model efficiency and data efficiency in transformer-based medical image analysis.

    Main Methods:

    • Proposed Light-UNETR with a Lightweight Dimension Reductive Attention (LIDR) module for feature extraction and a Compact Gated Linear Unit (CGLU) for parameter efficiency.
    • Introduced a Contextual Synergic Enhancement (CSE) strategy using Attention-Guided Replacement and Spatial Masking Consistency to improve learning from unlabeled data.

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    Main Results:

    • Light-UNETR demonstrated superior performance and efficiency across multiple benchmarks.
    • Achieved 1.43% higher Jaccard index on Left Atrial Segmentation using only 10% labeled data compared to BCP.
    • Reduced FLOPs by 90.8% and parameters by 85.8%.

    Conclusions:

    • Light-UNETR offers a highly efficient and effective solution for 3D medical image segmentation.
    • The proposed methods significantly improve data efficiency, enabling robust segmentation with limited labeled data.