AGE-US: automated gestational age estimation based on fetal ultrasound images

TL;DR

This paper introduces an interpretable deep learning method for automated gestational age estimation from fetal ultrasound images, improving accuracy and reducing complexity, especially in resource-limited settings.

Contribution

It presents a novel segmentation architecture and the use of distance maps to enhance gestational age estimation with limited annotated data.

Findings

Performance comparable to state-of-the-art models

Reduced model complexity for resource-constrained environments

Distance maps effectively estimate femur endpoints

Abstract

Being born small carries significant health risks, including increased neonatal mortality and a higher likelihood of future cardiac diseases. Accurate estimation of gestational age is critical for monitoring fetal growth, but traditional methods, such as estimation based on the last menstrual period, are in some situations difficult to obtain. While ultrasound-based approaches offer greater reliability, they rely on manual measurements that introduce variability. This study presents an interpretable deep learning-based method for automated gestational age calculation, leveraging a novel segmentation architecture and distance maps to overcome dataset limitations and the scarcity of segmentation masks. Our approach achieves performance comparable to state-of-the-art models while reducing complexity, making it particularly suitable for resource-constrained settings and with limited annotated data. Furthermore, our results demonstrate that the use of distance maps is particularly suitable for estimating femur endpoints.