Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Journal:
arXiv
Published Date:
Jul 1, 2025
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 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.
