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    This study introduces a new deep learning model for segmenting pathological fluid in optical coherence tomography (OCT) scans. The enhanced model accurately identifies intraretinal fluid, subretinal fluid, and pigment epithelial detachment, improving eye disease diagnosis.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Accurate segmentation of pathological fluid in optical coherence tomography (OCT) is crucial for diagnosing and treating eye diseases like neovascular age-related macular degeneration and diabetic macular edema.
    • Current fully convolutional neural network (FCN) architectures face challenges in handling the variability in location, size, and shape of fluid lesions and preserving their continuous, hole-free structure.

    Purpose of the Study:

    • To develop an FCN architecture for simultaneous segmentation of intraretinal fluid, subretinal fluid, and pigment epithelial detachment in OCT images.
    • To address limitations in current methods by incorporating multi-scale feature extraction and shape prior information.

    Main Methods:

    • Employed attention gate and spatial pyramid pooling modules within the FCN architecture to enhance multi-scale object extraction.
    • Introduced a novel curvature regularization term in the loss function to integrate shape prior information, ensuring lesion continuity.

    Main Results:

    • The proposed FCN architecture demonstrated significantly improved performance in segmenting pathological fluid lesions compared to state-of-the-art methods.
    • Evaluations on public and clinical OCT datasets confirmed the effectiveness and robustness of the developed method.

    Conclusions:

    • The novel FCN architecture effectively segments multiple types of pathological fluid in OCT, outperforming existing approaches.
    • Incorporating multi-scale processing and shape priors offers a promising direction for advancing automated analysis of retinal imaging data.