Accurate segmentation of polyps in the colorectal region is crucial for medical diagnosis and the localization of polyp areas. However, challenges arise from blurred boundaries due to the similarity between polyp edges and surrounding tissues, variable polyp morphology, and speckle noise. To address these challenges, we propose a Hybrid Multi-Frequency Perception Gated Selection Network (Polyp-Mamba) for precise polyp segmentation. First, we design a dual multi-frequency fusion encoder that employs Mamba and ResNet to quickly and effectively learn global and local features in polyp images. Specifically, we incorporate a novel Hybrid Multi-Frequency Fusion Module (HMFM) within the encoder, using discrete cosine transform to analyze features from multiple spectral perspectives. This approach mitigates the issue of blurred polyp boundaries caused by their similarity to surrounding tissues, effectively integrating local and global features. Additionally, we construct a Gated Selection Decoder to suppress irrelevant feature regions in the encoder and introduce deep supervision to guide decoder features to align closely with the labels. We conduct extensive experiments using five commonly used polyp test datasets. Comparisons with 14 state-of-the-art segmentation methods demonstrate that our approach surpasses traditional methods in sensitivity to different polyp images, robustness to variations in polyp size and shape, speckle noise, and distribution similarity between surrounding tissues and polyps. Overall, our method achieves superior mDice scores on five polyp test datasets compared to state-of-the-art methods, indicating better performance in polyp segmentation.

中文翻译：

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Polyp-Mamba：用于息肉分割的混合多频感知门控选择网络


准确分割结直肠区域息肉对于医学诊断和息肉区域的定位至关重要。然而，由于息肉边缘与周围组织之间的相似性、可变的息肉形态和斑点噪声，模糊的边界带来了挑战。为了应对这些挑战，我们提出了一种混合多频感知门控选择网络 （Polyp-Mamba） 用于精确息肉分割。首先，我们设计了一个双多频融合编码器，利用 Mamba 和 ResNet 快速有效地学习息肉图像中的全局和局部特征。具体来说，我们在编码器中集成了一种新的混合多频融合模块 （HMFM），使用离散余弦变换从多个频谱角度分析特征。这种方法缓解了由于息肉与周围组织的相似性而导致的息肉边界模糊的问题，有效地整合了局部和整体特征。此外，我们构建了一个门控选择解码器来抑制编码器中不相关的特征区域，并引入深度监督来指导解码器特征与标签紧密对齐。我们使用五个常用的息肉测试数据集进行了广泛的实验。与 14 种最先进的分割方法的比较表明，我们的方法在对不同息肉图像的敏感性、对息肉大小和形状变化的鲁棒性、斑点噪声以及周围组织和息肉之间的分布相似性方面超越了传统方法。总体而言，与最先进的方法相比，我们的方法在五个息肉测试数据集上获得了卓越的 mDice 分数，表明在息肉分割方面具有更好的性能。