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    Summary
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    This study introduces an automated framework for processing PET images, improving lesion detection and segmentation accuracy while reducing time and effort. The new method enhances medical image analysis for better disease identification.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Image Processing

    Background:

    • Segmentation of diseased tissue in Positron Emission Tomography (PET) images is currently time-consuming, labor-intensive, and lacks accuracy.
    • Existing methods struggle with efficient and precise identification of lesions in PET scans.

    Purpose of the Study:

    • To develop an automated framework for PET image screening, denoising, and diseased tissue segmentation.
    • To improve the accuracy and efficiency of lesion detection in PET imaging.

    Main Methods:

    • A differential activation filter was employed for screening whole-body PET images containing lesion tissue.
    • A novel neural network with residual connections was proposed for PET image reconstruction and denoising, outperforming standard Fully Convolutional Networks (FCNs).
    • A custom density-based clustering algorithm was utilized for segmenting lesion tissues from normal tissues.

    Main Results:

    • The automated framework demonstrated good performance and efficiency in PET lesion image screening, denoising, and segmentation.
    • Comparative tests against other algorithms showed superior results for the proposed framework.
    • The system achieved a favorable time cost for the entire image analysis process.

    Conclusions:

    • The developed automated framework offers a significant improvement over existing methods for PET image analysis.
    • The study highlights the potential of this framework for both scientific research and clinical applications in medical imaging.
    • The proposed approach shows promising prospects for enhancing diagnostic capabilities through improved PET image analysis.