提升医学图像分割边缘精度，融合多尺度提示与自适应门控机制
EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism
- 通过多频特征提取增强边缘感知能力
- 在ISIC2018数据集上达新最优性能
- 适合临床辅助诊断场景的高精度分割需求
医学图像分割对诊断、治疗规划和疾病监测至关重要，但常受模糊边界和背景噪声干扰。本文提出EEMS模型，结合边缘感知增强单元（EAEU）与多尺度提示生成单元（MSPGU）。EAEU通过多频特征提取增强边缘感知，精准刻画轮廓；MSPGU采用提示引导策略融合高层语义与低层空间特征，实现目标精确定位。双源自适应门控融合单元（DAGFU）将EAEU的边缘特征与MSPGU的语义特征融合，显著提升分割准确率与鲁棒性。在ISIC2018等数据集上的测试表明，EEMS性能优于现有方法，具备临床应用潜力。
原文摘要 · Abstract (English)
Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We introduce EEMS, a new model for segmentation, combining an Edge-Aware Enhancement Unit (EAEU) and a Multi-scale Prompt Generation Unit (MSPGU). EAEU enhances edge perception via multi-frequency feature extraction, accurately defining boundaries. MSPGU integrates high-level semantic and low-level spatial features using a prompt-guided approach, ensuring precise target localization. The Dual-Source Adaptive Gated Fusion Unit (DAGFU) merges edge features from EAEU with semantic features from MSPGU, enhancing segmentation accuracy and robustness. Tests on datasets like ISIC2018 confirm EEMS's superior performance and reliability as a clinical tool.
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