  Standard plane localization is crucial for ultrasound (US) diagnosis. In
prenatal US, dozens of standard planes are manually acquired with a 2D probe.
It is time-consuming and operator-dependent. In comparison, 3D US containing
multiple standard planes in one shot has the inherent advantages of less
user-dependency and more efficiency. However, manual plane localization in US
volume is challenging due to the huge search space and large fetal posture
variation. In this study, we propose a novel reinforcement learning (RL)
framework to automatically localize fetal brain standard planes in 3D US. Our
contribution is two-fold. First, we equip the RL framework with a
landmark-aware alignment module to provide warm start and strong spatial bounds
for the agent actions, thus ensuring its effectiveness. Second, instead of
passively and empirically terminating the agent inference, we propose a
recurrent neural network based strategy for active termination of the agent's
interaction procedure. This improves both the accuracy and efficiency of the
localization system. Extensively validated on our in-house large dataset, our
approach achieves the accuracy of 3.4mm/9.6{\deg} and 2.7mm/9.1{\deg} for the
transcerebellar and transthalamic plane localization, respectively. Ourproposed
RL framework is general and has the potential to improve the efficiency and
standardization of US scanning.
