提升超声图像分割边界精度，让SAM更懂医学影像细节。
An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation
- 多阶段特征提取+边缘感知监督，增强模型对轮廓的敏感度。
- 在多个数据集上显著优于现有SAM改进方法，边界分割更准。
- 适合需要高精度分割的超声诊断场景，如病变识别与定位。
超声图像分割对于勾勒解剖结构和病灶至关重要，是精准诊断的基础。尽管段落任意模型（SAM）在自然图像上表现优异，但在超声数据上的边界分割能力仍不足。为此，我们提出EP-SAM，一种面向超声图像的边缘感知且提示增强的SAM改进方法。具体地，通过图像编码器的多块特征提取，实现从粗到细的语义表征增强；同时引入边缘感知监督，提升模型对轮廓模糊与散斑噪声的鲁棒性。结合这些互补线索，EP-SAM生成高质量提示，有效引导模型聚焦目标区域。在多个基准数据集上的实验表明，EP-SAM始终优于现有基于SAM的方法。
原文摘要 · Abstract (English)
Ultrasound image segmentation is essential for delineating anatomical structures and lesions, providing the foundation for accurate diagnosis. While the Segment Anything Model (SAM) has demonstrated remarkable success on natural images, its performance on ultrasound data is often hindered by poor boundary delineation. To address this limitation, we propose EP-SAM, an edge-aware and prompt-enhanced adaptation of SAM. Specifically, we leverage multi-block feature extraction from the image encoder to enrich coarse-to-fine semantic representations, while edge-aware supervision of the image encoder improves robustness to contour ambiguity and speckle noise. By integrating these complementary cues, EP-SAM generates high-quality prompts that effectively guide the model toward target regions of interest. Experimental results on multiple benchmarks demonstrate that EP-SAM consistently outperforms existing SAM-based methods.
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