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JOANet: An Integrated Joint Optimization Architecture Making Medical Image Segmentation Really Helped by

Cheng-Hao Qiu, Xian-Shi Zhang, Yong-Jie Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 17, 2025
    PubMed
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    This study introduces a joint optimization method for medical image super-resolution and segmentation. The integrated approach improves segmentation accuracy on low-resolution images by enhancing relevant features.

    Area of Science:

    • Medical Image Analysis
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Traditional computer vision separates image enhancement and semantic tasks, optimizing for perception over utility.
    • This separation limits the effectiveness of enhancement techniques for downstream semantic applications like medical image segmentation.

    Purpose of the Study:

    • To propose an integrated joint optimization architecture for medical image super-resolution and segmentation.
    • To align enhancement objectives with the practical requirements of semantic tasks, specifically improving segmentation from super-resolved images.

    Main Methods:

    • Developed a novel joint architecture enabling simultaneous training of super-resolution and segmentation networks.
    • Implemented a super-resolution network guided by content reconstruction loss and segmentation-derived semantic loss.

    Related Experiment Videos

  • Prioritized semantically significant regions for reconstruction to benefit segmentation.

    • Main Results:

    • Jointly trained network significantly improved low-resolution medical image segmentation performance.
    • The proposed method outperformed traditional sequential approaches and even direct segmentation on high-resolution images.
    • Ablation studies confirmed the effectiveness of the joint optimization strategy.

    Conclusions:

    • Integrated joint optimization offers a superior framework for medical image analysis compared to isolated enhancement and semantic tasks.
    • The proposed architecture effectively bridges the gap between low-level enhancement and high-level semantic understanding.
    • This approach enhances computational utility by directly optimizing enhancement for specific semantic task requirements.