  Volumetric ultrasound has great potentials in promoting prenatal
examinations. Automated solutions are highly desired to efficiently and
effectively analyze the massive volumes. Segmentation and landmark localization
are two key techniques in making the quantitative evaluation of prenatal
ultrasound volumes available in clinic. However, both tasks are non-trivial
when considering the poor image quality, boundary ambiguity and anatomical
variations in volumetric ultrasound. In this paper, we propose an effective
framework for simultaneous segmentation and landmark localization in prenatal
ultrasound volumes. The proposed framework has two branches where informative
cues of segmentation and landmark localization can be propagated
bidirectionally to benefit both tasks. As landmark localization tends to suffer
from false positives, we propose a distance based loss to suppress the noise
and thus enhance the localization map and in turn the segmentation. Finally, we
further leverage an adversarial module to emphasize the correspondence between
segmentation and landmark localization. Extensively validated on a volumetric
ultrasound dataset of fetal femur, our proposed framework proves to be a
promising solution to facilitate the interpretation of prenatal ultrasound
volumes.
