Paper Info
Title
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
Abstract
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 3.4 mm/9.6. and 2.7 mm/9.1. for the transcerebellar and transthalamic plane localization, respectively. Our proposed RL framework is general and has the potential to improve the efficiency and standardization of US scanning.
Year
DOI
Venue
2019
10.1007/978-3-030-32254-0_33
Lecture Notes in Computer Science
DocType
Volume
ISSN
Conference
11768
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
11
Name
Order
Citations
PageRank
Haoran Dou1274.12
Xin Yang217512.96
Jikuan Qian302.37
Wufeng Xue435018.80
Hao Qin500.34
Xu Wang600.34
Lequan Yu770639.80
Shujun Wang8624.88
Yi Xiong902.37
Pheng-Ann Heng103565280.98
Dong Ni1136737.37