  Fetal growth assessment from ultrasound is based on a few biometric
measurements that are performed manually and assessed relative to the expected
gestational age. Reliable biometry estimation depends on the precise detection
of landmarks in standard ultrasound planes. Manual annotation can be
time-consuming and operator dependent task, and may results in high
measurements variability. Existing methods for automatic fetal biometry rely on
initial automatic fetal structure segmentation followed by geometric landmark
detection. However, segmentation annotations are time-consuming and may be
inaccurate, and landmark detection requires developing measurement-specific
geometric methods. This paper describes BiometryNet, an end-to-end landmark
regression framework for fetal biometry estimation that overcomes these
limitations. It includes a novel Dynamic Orientation Determination (DOD) method
for enforcing measurement-specific orientation consistency during network
training. DOD reduces variabilities in network training, increases landmark
localization accuracy, thus yields accurate and robust biometric measurements.
To validate our method, we assembled a dataset of 3,398 ultrasound images from
1,829 subjects acquired in three clinical sites with seven different ultrasound
devices. Comparison and cross-validation of three different biometric
measurements on two independent datasets shows that BiometryNet is robust and
yields accurate measurements whose errors are lower than the clinically
permissible errors, outperforming other existing automated biometry estimation
methods. Code is available at
https://github.com/netanellavisdris/fetalbiometry.
