用3D超声直接估胎儿出生体重，精度接近资深医生。
Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
- 首次直接从3D超声体积中估计胎儿体重，融合多尺度特征。
- 误差仅166.4±155.9克，相对误差5.1±4.6%，优于现有方法。
- 通过合成样本和半监督学习提升模型泛化能力，适合临床部署。
准确估算胎儿出生体重（FBW）对优化分娩决策、降低围产期死亡率至关重要。但现有临床方法效率低、依赖操作者且难以处理复杂胎儿解剖结构。现有深度学习方法基于2D标准超声图像或视频，缺乏空间信息，限制了预测精度。本研究提出首个直接从3D胎儿超声体积中估计FBW的方法。该方法结合多尺度特征融合网络（MFFN）与基于合成样本的学习框架（SSLF）。MFFN在稀疏监督下通过通道注意力、空间注意力及基于排序的损失函数有效提取并融合多尺度特征。SSLF通过简单拼接不同胎儿的头颅与腹部数据生成合成样本，利用半监督学习提升预测性能。实验结果表明，该方法表现优异，平均绝对误差为166.4±155.9克，平均绝对百分比误差为5.1±4.6%，超越现有方法，接近资深医生水平。代码已开源：https://github.com/Qioy-i/EFW。
原文摘要 · Abstract (English)
Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from 3D fetal US volumes. Our approach integrates a multi-scale feature fusion network (MFFN) and a synthetic sample-based learning framework (SSLF). The MFFN effectively extracts and fuses multi-scale features under sparse supervision by incorporating channel attention, spatial attention, and a ranking-based loss function. SSLF generates synthetic samples by simply combining fetal head and abdomen data from different fetuses, utilizing semi-supervised learning to improve prediction performance. Experimental results demonstrate that our method achieves superior performance, with a mean absolute error of $166.4\pm155.9$ $g$ and a mean absolute percentage error of $5.1\pm4.6$%, outperforming existing methods and approaching the accuracy of a senior doctor. Code is available at: https://github.com/Qioy-i/EFW.
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