智能机器人超声医师：基于互信息的少样本演示解耦奖励学习
Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations
AI总结：
本文提出智能机器人超声医师，通过互信息解耦奖励学习从少量专家演示中推断超声检查知识，并以由粗到细的导航方式实现自主超声采集，在多种体模和活体人颈动脉上验证了有效性。
摘要：
超声（US）成像由于具有实时性和无辐射等优势，被广泛用于生物特征测量和内脏器官诊断。然而，由于操作者之间存在差异，所得图像高度依赖于超声医师的经验。本文提出了一种智能机器人超声医师，通过向专家学习，自主“探索”目标解剖结构并将超声探头导航至相关的二维平面。专家潜在的高层生理知识通过神经奖励函数进行推断，采用自监督方式下的排序成对图像比较方法。这一过程可以理解为理解“超声检查的语言”。考虑到克服患者间差异的泛化能力，通过一个网络估计互信息，以在潜在空间中显式解耦任务相关特征和域特征。机器人定位基于与B模式图像相关的预测奖励，以由粗到细的方式进行。为验证所提出的奖励推断网络的有效性，分别在血管体模（“线”目标）、两种离体动物器官（鸡心脏和羊肾脏）体模（“点”目标）以及活体人颈动脉上进行了代表性实验。为进一步验证自主采集框架的性能，在三种体模（血管、鸡心脏和羊肾脏）上进行了物理机器人采集。结果表明，所提出的先进框架能够稳健地工作于多种已见和未见的体模以及活体人颈动脉数据上。
英文摘要：
Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to inter-operator variations, resulting images highly depend on the experience of sonographers. This work proposes an intelligent robotic sonographer to autonomously "explore" target anatomies and navigate a US probe to a relevant 2D plane by learning from the expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparisons approach in a self-supervised fashion. This process can be referred to as understanding the "language of sonography". Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated with B-mode images. To validate the effectiveness of the proposed reward inference network, representative experiments were performed on vascular phantoms ("line" target), two types of ex-vivo animal organs (chicken heart and lamb kidney) phantoms ("point" target) and in-vivo human carotids, respectively. To further validate the performance of the autonomous acquisition framework, physical robotic acquisitions were performed on three phantoms (vascular, chicken heart, and lamb kidney). The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in-vivo human carotid data.
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。
