Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound

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 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.