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Accurate standard plane (SP) localization is the fundamental step forprenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collectedto determine the clinical diagnosis. 2D US has to perform scanning for each SP,which is time-consuming and operator-dependent. While 3D US containing multipleSPs in one shot has the inherent advantages of less user-dependency and moreefficiency. Automatically locating SP in 3D US is very challenging due to thehuge search space and large fetal posture variations. Our previous studyproposed a deep reinforcement learning (RL) framework with an alignment moduleand active termination to localize SPs in 3D US automatically. However,termination of agent search in RL is important and affects the practicaldeployment. In this study, we enhance our previous RL framework with a newlydesigned adaptive dynamic termination to enable an early stop for the agentsearching, saving at most 67% inference time, thus boosting the accuracy andefficiency of the RL framework at the same time. Besides, we validate theeffectiveness and generalizability of our algorithm extensively on our in-housemulti-organ datasets containing 433 fetal brain volumes, 519 fetal abdomenvolumes, and 683 uterus volumes. Our approach achieves localization error of2.52mm/10.26 degrees, 2.48mm/10.39 degrees, 2.02mm/10.48 degrees, 2.00mm/14.57degrees, 2.61mm/9.71 degrees, 3.09mm/9.58 degrees, 1.49mm/7.54 degrees for thetranscerebellar, transventricular, transthalamic planes in fetal brain,abdominal plane in fetal abdomen, and mid-sagittal, transverse and coronalplanes in uterus, respectively. Experimental results show that our method isgeneral and has the potential to improve the efficiency and standardization ofUS scanning.

Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound