改进的U-Net模型提升超声图像胎儿头部分割精度
Attention-ResUNet for Automated Fetal Head Segmentation
- 融合残差连接与多尺度注意力门，增强关键区域识别
- 在HC18数据集上达99.30%平均Dice分数，优于多个基线模型
- 激活图显示解剖一致性，适合临床部署且计算效率高
超声图像中胎儿头部的自动分割对产前检查中的生物测量至关重要。尽管现有深度学习方法已取得较好效果，但仍面临低对比度、噪声和复杂解剖边界等挑战。本文提出Attention-ResUNet，通过在解码器四层集成注意力门，聚焦解剖相关区域并抑制背景噪声，同时结合残差连接促进梯度流动与特征复用。在n=200的HC18挑战数据集上，该方法取得99.30±0.14%的平均Dice分数，显著优于五种基线模型（ResUNet: 99.26%，Attention U-Net: 98.79%，Swin U-Net: 98.60%，Standard U-Net: 98.58%，U-Net++: 97.46%），统计分析显示差异极显著（p < 0.001），效应量为0.230至13.159（Cohen's d）。Saliency图分析表明其激活模式高度集中且解剖一致，具备良好可解释性。该方法以14.7M参数和45 GFLOPs推理成本实现新最优性能。
原文摘要 · Abstract (English)
Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning approaches have achieved a reasonable performance, they struggle with issues like low contrast, noise, and complex anatomical boundaries which are inherent to ultrasound imaging. This paper presents Attention-ResUNet. It is a novel architecture that synergistically combines residual learning with multi-scale attention mechanisms in order to achieve enhanced fetal head segmentation. Our approach integrates attention gates at four decoder levels to focus selectively on anatomically relevant regions while suppressing the background noise, and complemented by residual connections which facilitates gradient flow and feature reuse. Extensive evaluation on the HC18 Challenge dataset where n = 200 demonstrates that Attention ResUNet achieves a superior performance with a mean Dice score of 99.30 +/- 0.14% against similar architectures. It significantly outperforms five baseline architectures including ResUNet (99.26%), Attention U-Net (98.79%), Swin U-Net (98.60%), Standard U-Net (98.58%), and U-Net++ (97.46%). Through statistical analysis we confirm highly significant improvements (p < 0.001) with effect sizes that range from 0.230 to 13.159 (Cohen's d). Using Saliency map analysis, we reveal that our architecture produces highly concentrated, anatomically consistent activation patterns, which demonstrate an enhanced interpretability which is crucial for clinical deployment. The proposed method establishes a new state of the art performance for automated fetal head segmentation whilst maintaining computational efficiency with 14.7M parameters and a 45 GFLOPs inference cost. Code repository: https://github.com/Ammar-ss
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