  This paper presents a novel approach to automatic fetal brain biometry
motivated by needs in low- and medium- income countries. Specifically, we
leverage high-end (HE) ultrasound images to build a biometry solution for
low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised
domain adaptation approach to train deep models to be invariant to significant
image distribution shift between the image types. Our proposed method, which
employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial
pathways which enforce model invariance to; i) adversarial perturbations in the
feature space derived from LC images, and ii) appearance domain discrepancy.
Our Dual Adversarial Calibration method estimates transcerebellar diameter and
head circumference on images from low-cost ultrasound devices with a mean
absolute error (MAE) of 2.43mm and 1.65mm, compared with 7.28 mm and 5.65 mm
respectively for SOTA.
