  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.
