Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound

TL;DR

This paper presents a reinforcement learning framework for automatic standard plane localization in 3D ultrasound, utilizing tangent-point formulation, auxiliary task learning, and spatial-anatomical rewards to improve accuracy and robustness.

Contribution

It introduces a tangent-based RL formulation, auxiliary task learning, and spatial-anatomical rewards for enhanced 3D US standard plane localization.

Findings

Achieves high localization accuracy in uterus and fetal brain datasets.

Demonstrates robustness across different anatomical structures.

Reduces search space significantly with tangent-point formulation.

Abstract

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.