AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes

TL;DR

AutoFB introduces an automated framework that uses segmentation models to accurately estimate fetal biometry from standard ultrasound planes, reducing operator dependency and improving measurement consistency.

Contribution

The paper presents a unified automated system for fetal biometry estimation from ultrasound images, combining segmentation and region fitting, validated through extensive cross-validation.

Findings

Segmentation models are robust across different ultrasound images.

The automated biometry estimates are within clinically acceptable error margins.

Best segmentation performance correlates with higher biometry accuracy.

Abstract

During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a unified automated framework for estimating all measurements needed for the fetal weight assessment. The proposed framework semantically segments the key fetal anatomies using state-of-the-art segmentation models, followed by region fitting and scale recovery for the biometry estimation. We present an ablation study of segmentation algorithms to show their robustness through 4-fold cross-validation on a dataset of 349 ultrasound standard plane images from 42 pregnancies. Moreover, we show that the network with the best segmentation performance tends to be more accurate for biometry estimation. Furthermore, we demonstrate that the error between clinically measured and predicted fetal biometry is lower than the permissible error during routine clinical measurements.