论文标题
在3D超声波中搜索协作代理以获取多平面定位
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
论文作者
论文摘要
3D超声（US）由于其丰富的诊断信息，便携性和低成本而被广泛使用。美国体积中的自动化标准平面（SP）本地化不仅提高了效率并降低了用户依赖性，还可以提高3D美国的解释。在这项研究中，我们提出了一种新型的多代理增强学习（MARL）框架，以同时在3D US中定位多个子宫SP。我们的贡献是两个方面。首先，我们为MARL配备了一次性神经结构搜索（NAS）模块，以获得每个平面的最佳药物。具体而言，使用可区分体系结构采样器（GDA）的基于梯度的搜索用于加速和稳定训练过程。其次，我们提出了一种新颖的协作策略来加强代理商的交流。我们的策略使用复发性神经网络（RNN）来有效地学习SP之间的空间关系。在大型数据集上进行了广泛的验证，我们的方法分别达到了7.05度/2.21mm，8.62度/2.36mm和5.93度/0.89mm的准确性，分别为中间，横向和冠状平面定位。提出的MAL框架可以显着提高平面定位精度并降低计算成本和模型大小。
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.
