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    Summary

    This study introduces a novel framework for medical image segmentation, enabling unpaired multi-modal learning for better disease diagnosis. The method allows for single-modal image segmentation in clinical practice, overcoming data acquisition challenges.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Machine Learning

    Background:

    • Multi-modal Magnetic Resonance Imaging (MR)/Computed Tomography (CT) image segmentation is crucial for disease diagnosis and treatment.
    • Acquiring aligned multi-modal images is challenging due to high costs and contrast agent allergies.

    Purpose of the Study:

    • To develop a framework for unpaired multi-modal image segmentation.
    • To enable single-modal image segmentation during inference after training with unpaired data.

    Main Methods:

    • A synthesis-segmentation task-complementation network is proposed to mutually facilitate cross-modal image synthesis and segmentation.
    • A curvature consistency loss is introduced to maintain organ shape consistency between original and synthesized images.
    • A regression-segmentation task-complementation network is utilized for segmenting small lesions or substructures.

    Main Results:

    • The proposed framework demonstrated superior performance compared to state-of-the-art methods on both in-house and public datasets.
    • Experimental results validate the effectiveness of the synthesis-segmentation and regression-segmentation approaches.

    Conclusions:

    • The method effectively fuses dual-modal CT/MR images during training and requires only single-modal images for inference.
    • This approach is suitable for routine clinical use when only one imaging modality is available.