Robotic ultrasound has advanced local image-driven control, contact regulation, and view optimization, yet current systems lack the anatomical understanding needed to determine what to scan, where to begin, and how to adapt to individual patient anatomy. These gaps make systems still reliant on expert intervention to initiate scanning. Here we present SAMe, a semantic anatomy mapping engine that provides robotic ultrasound with an explicit anatomical prior layer. SAMe addresses scan initiation as a target-to-anatomy-to-action process: it grounds under-specified clinical complaints into structured target organs, instantiates a patient-specific anatomical representation for the grounded targets from a single external body image, and translates this representation into control-facing 6-DoF probe initialization states without any additional registration using preoperative CT or MRI. The anatomical representation maintained by SAMe is explicit, lightweight (single-organ inference in 0.08s), and compatible with downstream control by design. Across semantic grounding, anatomical instantiation, and real-robot evaluation, SAMe shows strong performance across the full initialization pipeline. In real-robot experiments, SAMe achieved overall organ-hit rates of 97.3% for liver initialization and 81.7% for kidney initialization across the evaluated target sets. Even when restricted to the centroid target, SAMe outperformed the surface-heuristic baseline for both liver and kidney initialization. These results establish an explicit anatomical prior layer that addresses scan initialization and is designed to support broader downstream autonomous scanning pipelines, providing the anatomical foundation for complaint-driven, anatomically informed robotic ultrasonography.

翻译：机器人超声已实现局部图像驱动控制、接触调节与视图优化，但现有系统缺乏理解解剖结构的能力，无法确定扫描目标、起始部位及适应个体患者解剖差异。这些不足使得系统仍需专家介入才能启动扫描。本文提出SAMe——一种语义解剖映射引擎，为机器人超声提供显式解剖先验层。SAMe将扫描启动问题转化为"目标-解剖-动作"流程：将模糊的临床主诉映射为结构化目标器官，基于单张体表图像为映射目标生成患者特异性解剖表征，并无需术前CT或MRI配准即可将该表征转化为面向控制的6自由度探头初始化状态。SAMe维护的解剖表征具有显式性、轻量化（单器官推理仅需0.08秒）及与下游控制架构天然兼容的特性。在语义接地、解剖实例化及真实机器人评估中，SAMe在整个初始化流程中表现优异。真实机器人实验中，SAMe在评估目标集上对肝脏初始化的器官命中率达97.3%，肾脏初始化为81.7%。即使限制于质心目标，SAMe在肝脏与肾脏初始化中均优于表面启发式基线。这些结果验证了显式解剖先验层在解决扫描初始化问题上的有效性，其设计支持更广泛的下游自主扫描流程，为基于临床主诉的解剖感知机器人超声检查奠定了解剖学基础。