Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound
Xin Yang, Haoran Dou, Ruobing Huang, Wufeng Xue, Yuhao Huang, Jikuan, Qian, Yuanji Zhang, Huanjia Luo, Huizhi Guo, Tianfu Wang, Yi Xiong, Dong Ni

TL;DR
This paper introduces an enhanced deep reinforcement learning framework with adaptive dynamic termination for automatic standard plane localization in 3D ultrasound, significantly improving efficiency and accuracy across multiple fetal organs.
Contribution
It proposes an adaptive dynamic termination method for RL-based plane localization, reducing inference time by up to 67% and demonstrating broad applicability on multi-organ fetal ultrasound datasets.
Findings
Achieved localization errors around 2-3mm and 9-14 degrees across various fetal planes.
Reduced inference time by up to 67% with adaptive termination.
Validated generalizability on diverse multi-organ datasets.
Abstract
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…
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Taxonomy
TopicsFetal and Pediatric Neurological Disorders · Domain Adaptation and Few-Shot Learning · Cleft Lip and Palate Research
