提升产科超声中胎儿头位进展角的测量精度与稳定性
R2AoP: Reliable and Robust Angle of Progression Estimation from Intrapartum Ultrasound
- 通过结构感知分割与置信度加权拟合，减少边界模糊带来的误差
- 在多中心数据集上，AoP估计误差和边界指标均显著优于现有方法
- 无需目标标注即可适应不同设备条件，适合临床实时应用
从分娩期经会阴超声准确估计胎儿头位进展角（AoP）对客观评估产程至关重要，但易受成像噪声、边界模糊及局部分割误差几何放大影响。本文提出R2AoP框架，融合结构引导分割与置信度驱动几何建模，实现稳定可重复的测量。采用三分支结构增强型主干网络提升耻骨联合（PS）与胎头（FH）轮廓分割精度；置信度加权轮廓拟合有效抑制不可靠边界点对AoP计算的影响。为进一步提升异质采集条件下的性能，引入轻量级几何鲁棒性测试时自适应策略，无需目标标注即可实现稳定推理。在多中心基准数据集上的大量实验表明，相比当前最优方法，本方法在AoP误差和边界指标上均有持续降低。代码已开源：https://github.com/baiyou1234/R2AoP。
原文摘要 · Abstract (English)
Accurate estimation of the Angle of Progression (AoP) from intrapartum transperineal ultrasound is critical for objective assessment of labor progression, yet remains highly sensitive to imaging noise, boundary ambiguities, and the geometric amplification of local segmentation errors. We propose R2AoP, a reliable and robust AoP estimation framework that integrates structurally informed segmentation and confidence-guided geometric modeling to achieve stable and reproducible measurements. A three-branch local-structure-enhanced backbone improves the delineation of the pubic symphysis (PS) and fetal head (FH), while confidence-weighted contour fitting explicitly suppresses the influence of unreliable boundary points in AoP computation. To further improve performance under heterogeneous acquisition conditions, we introduce a lightweight geometry-reliable test-time adaptation strategy as an auxiliary component, enabling stable inference without target annotations. Extensive evaluations on multi-center benchmarks demonstrate consistent reductions in AoP error and boundary metrics compared with state-of-the-art AoP methods. Our source code is available at https://github.com/baiyou1234/R2AoP.
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