  3D ultrasound (US) is widely used due to its rich diagnostic information,
portability and low cost. Automated standard plane (SP) localization in US
volume not only improves efficiency and reduces user-dependence, but also
boosts 3D US interpretation. In this study, we propose a novel Multi-Agent
Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D
US simultaneously. Our contribution is two-fold. First, we equip the MARL with
a one-shot neural architecture search (NAS) module to obtain the optimal agent
for each plane. Specifically, Gradient-based search using Differentiable
Architecture Sampler (GDAS) is employed to accelerate and stabilize the
training process. Second, we propose a novel collaborative strategy to
strengthen agents' communication. Our strategy uses recurrent neural network
(RNN) to learn the spatial relationship among SPs effectively. Extensively
validated on a large dataset, our approach achieves the accuracy of 7.05
degree/2.21mm, 8.62 degree/2.36mm and 5.93 degree/0.89mm for the mid-sagittal,
transverse and coronal plane localization, respectively. The proposed MARL
framework can significantly increase the plane localization accuracy and reduce
the computational cost and model size.
