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Frequency-Phase Guided Attention Complex-Valued Network for Ultrasound Image Segmentation.

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    A novel Frequency Phase-Guided Attention Network (FPGANet) improves ultrasound image segmentation by utilizing complex-valued models. This method enhances lesion identification and diagnostic accuracy in medical imaging.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Automatic segmentation of ultrasound images is vital for diagnosis but challenging due to speckle noise and low contrast.
    • Complex-value-based neural networks offer potential for improved structure perception by processing phase information.

    Purpose of the Study:

    • To develop a Frequency Phase-Guided Attention Network (FPGANet) for enhanced ultrasound image segmentation.
    • To leverage complex-valued models with phase and frequency perspectives for improved segmentation accuracy.

    Main Methods:

    • Input ultrasound images are transformed into the complex domain for processing by a complex-valued model.
    • A complex hybrid attention module refines phase component perception, and a frequency-adaptive separation module emphasizes frequency features using wavelet decomposition and frequency channel attention.

    Main Results:

    • FPGANet demonstrated superior performance in segmenting breast, cardiac, thyroid, and abdominal effusion ultrasound images.
    • Comparative experiments against state-of-the-art methods confirmed the effectiveness of the proposed FPGANet.

    Conclusions:

    • The developed FPGANet shows significant potential for advancing automatic ultrasound image segmentation.
    • The integration of phase and frequency information in a complex-valued framework improves diagnostic capabilities.