<ul data-eligibleForWebStory="false"><li>Polyp segmentation in colonoscopy images is essential for early detection of colorectal cancer, but it remains challenging due to variations in polyp characteristics and indistinct boundaries.</li><li>Existing CNN and transformer-based methods struggle with weak or blurry boundary segmentation and lack generalizability for real-time clinical use.</li><li>A new approach called SAM-MaGuP has been introduced to address these limitations, incorporating a boundary distillation module and a Mamba adapter within the Segment Anything Model (SAM).</li><li>SAM-MaGuP has shown superior segmentation accuracy over current methods by leveraging a Mamba-guided boundary prior and a 1D-2D Mamba block, setting a new standard in polyp segmentation.</li></ul>

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation

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