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    This study introduces a novel generative adversarial network (GAN) for enhanced super-resolution and segmentation of retinal layers in Optical Coherence Tomography (OCT) scans, aiding Alzheimer's disease biomarker discovery.

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    Area of Science:

    • Ophthalmology and Medical Imaging
    • Artificial Intelligence in Healthcare
    • Neurodegenerative Disease Research

    Background:

    • Optical coherence tomography (OCT) is a promising non-invasive tool for Alzheimer's disease (AD) diagnosis.
    • Retinal layer thickness in OCT scans is hypothesized as a potential AD biomarker.
    • Accurate segmentation of retinal layers is crucial for biomarker analysis.

    Purpose of the Study:

    • To develop an advanced generative adversarial network (GAN) for simultaneous super-resolution and segmentation of retinal layers in OCT scans.
    • To improve the clarity and accuracy of OCT image analysis for potential AD biomarkers.
    • To introduce a Multi-Stage, Multi-Discriminatory GAN (MultiSDGAN) for robust OCT domain translation and segmentation.

    Main Methods:

    • Proposed a Multi-Stage, Multi-Discriminatory Generative Adversarial Network (MultiSDGAN) for super-resolution and segmentation.
    • Employed adversarial training with multiple discriminators across multiple stages to prevent model saturation.
    • Incorporated Dice loss for segmentation enhancement and Structured Similarity Index Measure (SSIM) for super-resolution improvement.

    Main Results:

    • The MultiSDGAN achieved significant improvements in reducing the equal error rate (44.24% and 34.09% relative improvements) with ten-fold cross-validation.
    • Addition of Dice loss and SSIM as loss functions significantly enhanced segmentation accuracy (31.33% relative improvement).
    • The proposed method demonstrates superior performance in OCT super-resolution and retinal layer segmentation.

    Conclusions:

    • The developed MultiSDGAN offers a powerful approach for accurate retinal layer segmentation and super-resolution in OCT scans.
    • This technique holds potential for advancing Alzheimer's disease diagnosis and progression monitoring through improved biomarker identification.
    • The integration of specialized loss functions further refines the model's performance in both segmentation and image enhancement.