  3D ultrasound (US) has become prevalent due to its rich spatial and
diagnostic information not contained in 2D US. Moreover, 3D US can contain
multiple standard planes (SPs) in one shot. Thus, automatically localizing SPs
in 3D US has the potential to improve user-independence and
scanning-efficiency. However, manual SP localization in 3D US is challenging
because of the low image quality, huge search space and large anatomical
variability. In this work, we propose a novel multi-agent reinforcement
learning (MARL) framework to simultaneously localize multiple SPs in 3D US. Our
contribution is four-fold. First, our proposed method is general and it can
accurately localize multiple SPs in different challenging US datasets. Second,
we equip the MARL system with a recurrent neural network (RNN) based
collaborative module, which can strengthen the communication among agents and
learn the spatial relationship among planes effectively. Third, we explore to
adopt the neural architecture search (NAS) to automatically design the network
architecture of both the agents and the collaborative module. Last, we believe
we are the first to realize automatic SP localization in pelvic US volumes, and
note that our approach can handle both normal and abnormal uterus cases.
Extensively validated on two challenging datasets of the uterus and fetal
brain, our proposed method achieves the average localization accuracy of 7.03
degrees/1.59mm and 9.75 degrees/1.19mm. Experimental results show that our
light-weight MARL model has higher accuracy than state-of-the-art methods.
