Mamba引导的边界先验至关重要：广义息肉分割的新视角
Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation
AI总结：
本文提出SAM-MaGuP方法，通过在SAM中引入边界蒸馏模块和1D-2D Mamba适配器，利用Mamba引导的边界先验解决弱边界息肉分割难题，在多个数据集上取得领先性能。
摘要：
结肠镜图像中的息肉分割对于结直肠癌的早期检测和诊断至关重要。然而，由于息肉在形状、大小和颜色上的显著差异，以及息肉与周围组织之间的高度相似性，并且常常伴有不清晰的边界，该任务仍然是一个重大挑战。尽管现有的基于编码器-解码器CNN和基于Transformer的方法已显示出有前景的结果，但它们在边界较弱或模糊的息肉上难以实现稳定的分割性能。这些方法在区分息肉与非息肉以及捕获关键边界线索方面的能力有限。此外，它们的泛化能力仍不能满足实时临床应用的需求。为了解决这些局限性，我们提出了SAM-MaGuP，这是一种用于鲁棒息肉分割的突破性方法。通过在Segment Anything Model（SAM）中引入边界蒸馏模块和1D-2D Mamba适配器，SAM-MaGuP擅长解决弱边界挑战，并通过丰富的全局上下文交互增强特征学习。在五个不同数据集上的广泛评估表明，SAM-MaGuP优于最先进的方法，实现了无与伦比的分割精度和鲁棒性。我们的关键创新——Mamba引导的边界先验和1D-2D Mamba模块——在该领域树立了新的基准，将息肉分割的边界推向了新的高度。
英文摘要：
Polyp segmentation in colonoscopy images is crucial for early detection and diagnosis of colorectal cancer. However, this task remains a significant challenge due to the substantial variations in polyp shape, size, and color, as well as the high similarity between polyps and surrounding tissues, often compounded by indistinct boundaries. While existing encoder-decoder CNN and transformer-based approaches have shown promising results, they struggle with stable segmentation performance on polyps with weak or blurry boundaries. These methods exhibit limited abilities to distinguish between polyps and non-polyps and capture essential boundary cues. Moreover, their generalizability still falls short of meeting the demands of real-time clinical applications. To address these limitations, we propose SAM-MaGuP, a groundbreaking approach for robust polyp segmentation. By incorporating a boundary distillation module and a 1D-2D Mamba adapter within the Segment Anything Model (SAM), SAM-MaGuP excels at resolving weak boundary challenges and amplifies feature learning through enriched global contextual interactions. Extensive evaluations across five diverse datasets reveal that SAM-MaGuP outperforms state-of-the-art methods, achieving unmatched segmentation accuracy and robustness. Our key innovations, a Mamba-guided boundary prior and a 1D-2D Mamba block, set a new benchmark in the field, pushing the boundaries of polyp segmentation to new heights.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
