具有热启动与主动终止能力的智能体用于3D超声中的平面定位
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
AI总结：
提出带地标感知热启动与RNN主动终止机制的强化学习框架，实现3D超声中胎儿脑标准平面的自动高精度定位。
摘要：
标准平面定位对超声（US）诊断至关重要。在产前超声中，数十个标准平面需使用2D探头手动采集，既耗时又依赖操作者。相比之下，一次采集即可包含多个标准平面的3D超声具有减少用户依赖、提高效率的内在优势。然而，由于搜索空间巨大且胎儿姿势变化显著，在超声体数据中手动进行平面定位颇具挑战。本研究提出一种新颖的强化学习（RL）框架，用于在3D超声中自动定位胎儿脑标准平面。我们的贡献有两方面：第一，为RL框架配备地标感知对齐模块，为智能体动作提供热启动和强空间约束，从而保证其有效性；第二，不再被动且经验性地终止智能体推理，而是提出一种基于循环神经网络的策略，主动终止智能体的交互过程，从而同时提升定位系统的准确性和效率。在我们自建的大规模数据集上进行广泛验证后，该方法在经小脑平面和经丘脑平面定位中分别达到3.4 mm/9.6°和2.7 mm/9.1°的精度。所提出的RL框架具有通用性，并有潜力提升超声扫描的效率和标准化水平。
英文摘要：
Standard plane localization is crucial for ultrasound (US) diagnosis. In prenatal US, dozens of standard planes are manually acquired with a 2D probe. It is time-consuming and operator-dependent. In comparison, 3D US containing multiple standard planes in one shot has the inherent advantages of less user-dependency and more efficiency. However, manual plane localization in US volume is challenging due to the huge search space and large fetal posture variation. In this study, we propose a novel reinforcement learning (RL) framework to automatically localize fetal brain standard planes in 3D US. Our contribution is two-fold. First, we equip the RL framework with a landmark-aware alignment module to provide warm start and strong spatial bounds for the agent actions, thus ensuring its effectiveness. Second, instead of passively and empirically terminating the agent inference, we propose a recurrent neural network based strategy for active termination of the agent's interaction procedure. This improves both the accuracy and efficiency of the localization system. Extensively validated on our in-house large dataset, our approach achieves the accuracy of 3.4mm/9.6° and 2.7mm/9.1° for the transcerebellar and transthalamic plane localization, respectively. Ourproposed RL framework is general and has the potential to improve the efficiency and standardization of US scanning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
