EdgeSegDiff: Edge-Conditional Diffusion Model for Skin Lesion Segmentation

doi:10.1007/978-981-95-5631-1_26

2026-01-01

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DOI：10.1007/978-981-95-5631-1_26

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边缘信息在具有复杂边界的医学图像分割任务中至关重要，例如皮肤病变分割，其中精确的边缘建模直接影响分割精度。虽然去噪扩散概率模型（DDPMs）在医学图像分割中显示出巨大潜力，因其能够精细地细化细节，但现有方法通常仅依赖原始图像的全局特征作为条件输入，未能显式地在去噪过程中融入像素级边缘先验。这一限制制约了分割精度，尤其是在低对比度过渡区域。为解决此问题，我们提出了EdgeSegDiff，一种基于边缘条件引导策略的皮肤病变分割扩散模型。该模型引入边缘图作为条件信息，并设计了一种联合损失函数以动态调节对边缘先验的依赖。此外，引入了边缘特征增强（EFE）模块，结合特征差异化和SimAM注意力机制以增强边缘特征表示。为有效抑制条件输入中的高频噪声干扰同时保留关键边缘细节，进一步提出了基于小波变换和动态阈值的High-Frequency Noise Filter（HF-Filter）。在公开的ISIC-2016、ISIC-2017和ISIC-2018数据集上，EdgeSegDiff分别实现了92.1%、87.4%和90.8%的Dice系数，证明了其在皮肤病变分割任务中的优越性和鲁棒性。

Keyword:

Skin Lesion Segmentation

Diffusion Models

Edge-Conditional Guidance

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PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2025, PT XIV

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sichuan normal university

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