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    Summary

    We introduce Serp-Mamba, a novel network for segmenting retinal vessels in Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images. This method effectively handles high-resolution data and improves diagnostic accuracy for fundus diseases.

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    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • Accurate retinal vessel segmentation is crucial for diagnosing fundus diseases.
    • Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) provides extensive retinal views but presents segmentation challenges due to high resolution and complex vessel structures.
    • Existing methods struggle with preserving vessel continuity and addressing class imbalance in UWF-SLO images.

    Purpose of the Study:

    • To develop an advanced deep learning network for precise retinal vessel segmentation in high-resolution UWF-SLO images.
    • To leverage the strengths of Mamba's selective State Space Model (SSM) for modeling long-range dependencies in vascular structures.
    • To introduce novel mechanisms for handling the intricate, curved nature of vessels and the significant class imbalance in UWF-SLO imagery.

    Main Methods:

    • Proposed the Serpentine Mamba (Serp-Mamba) network, integrating Mamba's SSM for efficient long-range dependency modeling.
    • Developed a Serpentine Interwoven Adaptive (SIA) scan mechanism to follow curved vessel structures, ensuring continuous feature capture.
    • Introduced an Ambiguity-Driven Dual Recalibration (ADDR) module to mitigate class imbalance by refining ambiguous pixel delineations.

    Main Results:

    • Serp-Mamba demonstrated superior performance in high-resolution retinal vessel segmentation across three independent datasets.
    • Ablation studies confirmed the significant contribution of the SIA scan and ADDR module to the network's effectiveness.
    • The proposed methods successfully addressed challenges related to vessel continuity and class imbalance inherent in UWF-SLO imaging.

    Conclusions:

    • The Serp-Mamba network offers a robust and effective solution for retinal vessel segmentation in UWF-SLO images.
    • The SIA scan and ADDR module are key innovations for improving segmentation accuracy and handling image complexities.
    • This work advances the potential for automated detection and diagnosis of fundus diseases using high-resolution retinal imaging.