标题： 准确且高效的三维超声胎儿出生体重估计 显示英文标题

标题： Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound

摘要： 准确的胎儿出生体重（FBW）估计对于优化分娩决策和减少围产期死亡率至关重要。 然而，现有的临床方法在FBW估计中效率低下、依赖操作者，并且在复杂胎儿解剖情况下难以应用。 现有的深度学习方法基于缺乏空间信息的2D标准超声（US）图像或视频，限制了其预测准确性。 在本研究中，我们提出了第一个直接从3D胎儿US体积估计FBW的方法。 我们的方法集成了多尺度特征融合网络（MFFN）和基于合成样本的学习框架（SSLF）。 MFFN通过结合通道注意力、空间注意力和基于排名的损失函数，在稀疏监督下有效提取和融合多尺度特征。 SSLF通过简单地将不同胎儿的头部和腹部数据组合生成合成样本，利用半监督学习提高预测性能。 实验结果表明，我们的方法表现出色，平均绝对误差为$166.4\pm155.9$ $g$ ，平均绝对百分比误差为$5.1\pm4.6$%，优于现有方法，并接近资深医生的准确性。 代码可在以下地址获得：https://github.com/Qioy-i/EFW. 显示英文摘要

摘要： 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.