  Automatic segmentation of anatomical landmarks from ultrasound (US) plays an
important role in the management of preterm neonates with a very low birth
weight due to the increased risk of developing intraventricular hemorrhage
(IVH) or other complications. One major problem in developing an automatic
segmentation method for this task is the limited availability of annotated
data. To tackle this issue, we propose a novel image synthesis method using
multi-scale self attention generator to synthesize US images from various
segmentation masks. We show that our method can synthesize high-quality US
images for every manipulated segmentation label with qualitative and
quantitative improvements over the recent state-of-the-art synthesis methods.
Furthermore, for the segmentation task, we propose a novel method, called
Confidence-guided Brain Anatomy Segmentation (CBAS) network, where segmentation
and corresponding confidence maps are estimated at different scales. In
addition, we introduce a technique which guides CBAS to learn the weights based
on the confidence measure about the estimate. Extensive experiments demonstrate
that the proposed method for both synthesis and segmentation tasks achieve
significant improvements over the recent state-of-the-art methods. In
particular, we show that the new synthesis framework can be used to generate
realistic US images which can be used to improve the performance of a
segmentation algorithm.
