Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Jian Wang, Qiongying Ni, Hongkui Yu, Ruixuan Yao, Jinqiao Ying, Bin Zhang, Xingyi Yang, Jin Peng, Jiongquan Chen, Junxuan Yu, Wenlong Shi, Chaoyu Chen, Zhongnuo Yan, Mingyuan Luo, Gaocheng Cai, Dong Ni, Jing Lu, Xin Yang

TL;DR
This paper introduces a novel deep learning approach for fetal birth weight estimation directly from 3D ultrasound volumes, combining multi-scale feature fusion and synthetic sample learning to improve accuracy and efficiency over existing methods.
Contribution
The study presents the first method for direct FBW estimation from 3D US volumes using a multi-scale feature fusion network and semi-supervised synthetic sample generation.
Findings
Achieved mean absolute error of 166.4g, close to senior doctor accuracy.
Outperformed existing 2D ultrasound-based methods.
Demonstrated robustness and improved prediction accuracy.
Abstract
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…
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