  Accurate standard plane (SP) localization is the fundamental step for
prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected
to determine the clinical diagnosis. 2D US has to perform scanning for each SP,
which is time-consuming and operator-dependent. While 3D US containing multiple
SPs in one shot has the inherent advantages of less user-dependency and more
efficiency. Automatically locating SP in 3D US is very challenging due to the
huge search space and large fetal posture variations. Our previous study
proposed a deep reinforcement learning (RL) framework with an alignment module
and active termination to localize SPs in 3D US automatically. However,
termination of agent search in RL is important and affects the practical
deployment. In this study, we enhance our previous RL framework with a newly
designed adaptive dynamic termination to enable an early stop for the agent
searching, saving at most 67% inference time, thus boosting the accuracy and
efficiency of the RL framework at the same time. Besides, we validate the
effectiveness and generalizability of our algorithm extensively on our in-house
multi-organ datasets containing 433 fetal brain volumes, 519 fetal abdomen
volumes, and 683 uterus volumes. Our approach achieves localization error of
2.52mm/10.26 degrees, 2.48mm/10.39 degrees, 2.02mm/10.48 degrees, 2.00mm/14.57
degrees, 2.61mm/9.71 degrees, 3.09mm/9.58 degrees, 1.49mm/7.54 degrees for the
transcerebellar, transventricular, transthalamic planes in fetal brain,
abdominal plane in fetal abdomen, and mid-sagittal, transverse and coronal
planes in uterus, respectively. Experimental results show that our method is
general and has the potential to improve the efficiency and standardization of
US scanning.
