Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Accurate gestational age (GA) estimation, ideally through fetal ultrasound
measurement, is a crucial aspect of providing excellent antenatal care.
However, deriving GA from manual fetal biometric measurements depends on the
operator and is time-consuming. Hence, automatic computer-assisted methods are
demanded in clinical practice. In this paper, we present a novel feature fusion
framework to estimate GA using fetal ultrasound images without any measurement
information. We adopt a deep learning model to extract deep representations
from ultrasound images. We extract radiomic features to reveal patterns and
characteristics of fetal brain growth. To harness the interpretability of
radiomics in medical imaging analysis, we estimate GA by fusing radiomic
features and deep representations. Our framework estimates GA with a mean
absolute error of 8.0 days across three trimesters, outperforming current
machine learning-based methods at these gestational ages. Experimental results
demonstrate the robustness of our framework across different populations in
diverse geographical regions. Our code is publicly available on
\href{https://github.com/13204942/RadiomicsImageFusion_FetalUS}.
