  Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a
key biometric for monitoring the healthy development of fetuses. Unfortunately,
such measurements are subject to large inter-observer variability, resulting in
low early-detection rates of fetal abnormalities. To address this issue, we
propose a novel probabilistic Deep Learning approach for real-time automated
estimation of fetal HC. This system feeds back statistics on measurement
robustness to inform users how confident a deep neural network is in evaluating
suitable views acquired during free-hand ultrasound examination. In real-time
scenarios, this approach may be exploited to guide operators to scan planes
that are as close as possible to the underlying distribution of training
images, for the purpose of improving inter-operator consistency. We train on
free-hand ultrasound data from over 2000 subjects (2848 training/540 test) and
show that our method is able to predict HC measurements within 1.81$\pm$1.65mm
deviation from the ground truth, with 50% of the test images fully contained
within the predicted confidence margins, and an average of 1.82$\pm$1.78mm
deviation from the margin for the remaining cases that are not fully contained.
