  Standard plane (SP) localization is essential in routine clinical ultrasound
(US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in
one scan and provide complete anatomy with the addition of coronal plane.
However, manually navigating SPs in 3D US is laborious and biased due to the
orientation variability and huge search space. In this study, we introduce a
novel reinforcement learning (RL) framework for automatic SP localization in 3D
US. Our contribution is three-fold. First, we formulate SP localization in 3D
US as a tangent-point-based problem in RL to restructure the action space and
significantly reduce the search space. Second, we design an auxiliary task
learning strategy to enhance the model's ability to recognize subtle
differences crossing Non-SPs and SPs in plane search. Finally, we propose a
spatial-anatomical reward to effectively guide learning trajectories by
exploiting spatial and anatomical information simultaneously. We explore the
efficacy of our approach on localizing four SPs on uterus and fetal brain
datasets. The experiments indicate that our approach achieves a high
localization accuracy as well as robust performance.
