专为超声图像设计的轻量分割网络，精准分割边界且计算高效。
USEANet: Ultrasound-Specific Edge-Aware Multi-Branch Network for Lightweight Medical Image Segmentation
- 多分支结构分别处理降噪、边缘增强和对比度提升。
- 在BUSI数据集上达到67.01的IoU，参数仅364万，算力需求0.79G。
- 适合实时临床应用，尤其对资源受限设备有重要意义。
超声图像分割面临斑点噪声、低对比度和边界模糊等独特挑战，而临床部署要求模型计算高效。本文提出USEANet，一种面向超声的边缘感知多分支网络，通过四项创新实现性能与效率的最佳平衡：(1) 超声特异性多分支处理，包含专用模块用于降噪、边缘增强和对比度提升；(2) 边缘感知注意力机制，在极小计算开销下聚焦边界信息；(3) 分层特征聚合与自适应权重学习；(4) 面向超声的解码器增强以优化分割精度。基于超轻量级PVT-B0主干网络，USEANet在五个超声数据集上显著优于现有方法，仅需3.64M参数和0.79G FLOPs。实验表明其在BUSI数据集上达到67.01 IoU，显著超越传统方法，同时保持极佳的计算效率，适用于实时临床场景。代码已开源。
原文摘要 · Abstract (English)
Ultrasound image segmentation faces unique challenges including speckle noise, low contrast, and ambiguous boundaries, while clinical deployment demands computationally efficient models. We propose USEANet, an ultrasound-specific edge-aware multi-branch network that achieves optimal performance-efficiency balance through four key innovations: (1) ultrasound-specific multi-branch processing with specialized modules for noise reduction, edge enhancement, and contrast improvement; (2) edge-aware attention mechanisms that focus on boundary information with minimal computational overhead; (3) hierarchical feature aggregation with adaptive weight learning; and (4) ultrasound-aware decoder enhancement for optimal segmentation refinement. Built on an ultra-lightweight PVT-B0 backbone, USEANet significantly outperforms existing methods across five ultrasound datasets while using only 3.64M parameters and 0.79G FLOPs. Experimental results demonstrate superior segmentation accuracy with 67.01 IoU on BUSI dataset, representing substantial improvements over traditional approaches while maintaining exceptional computational efficiency suitable for real-time clinical applications. Code is available at https://github.com/chouheiwa/USEANet.
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