面向机器人肋间超声成像中自主US-CT配准的类别感知软骨分割
Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging
AI总结：
本文提出类别感知软骨分割网络与基于稠密骨架图的非刚性配准方法，将肋间超声扫描路径从通用模板精确映射到个体患者，以提高机器人肋间超声成像质量。
摘要：
超声成像因便携、实时且无辐射等优势，已在临床检查中广泛使用。考虑到自主检查系统在医院大规模部署的潜力，机器人超声成像受到越来越多的关注。然而，由于患者间差异，为每位患者规划最优路径仍具挑战性，尤其是对声窗有限的胸部应用，例如肋间肝脏成像。为解决该问题，本文提出一种带几何约束后处理的类别感知软骨分割网络，用于获取患者特异性肋骨骨架。随后，提出一种基于稠密骨架图的非刚性配准方法，将肋间扫描路径从通用模板映射到个体患者。通过显式考虑高声阻抗骨结构，转移后的扫描路径可精确定位于肋间间隙，从而减少声影并提高内部器官的可视性。为评估所提方法，最终路径映射性能在五个不同CT和两名志愿者超声数据上进行验证，形成十对CT-US组合。结果表明，所提出的基于图的配准方法能够稳健且精确地将路径从CT模板映射到个体患者（欧氏误差：$2.21\pm1.11~mm$）。
英文摘要：
Ultrasound imaging has been widely used in clinical examinations owing to the advantages of being portable, real-time, and radiation-free. Considering the potential of extensive deployment of autonomous examination systems in hospitals, robotic US imaging has attracted increased attention. However, due to the inter-patient variations, it is still challenging to have an optimal path for each patient, particularly for thoracic applications with limited acoustic windows, e.g., intercostal liver imaging. To address this problem, a class-aware cartilage bone segmentation network with geometry-constraint post-processing is presented to capture patient-specific rib skeletons. Then, a dense skeleton graph-based non-rigid registration is presented to map the intercostal scanning path from a generic template to individual patients. By explicitly considering the high-acoustic impedance bone structures, the transferred scanning path can be precisely located in the intercostal space, enhancing the visibility of internal organs by reducing the acoustic shadow. To evaluate the proposed approach, the final path mapping performance is validated on five distinct CTs and two volunteer US data, resulting in ten pairs of CT-US combinations. Results demonstrate that the proposed graph-based registration method can robustly and precisely map the path from CT template to individual patients (Euclidean error: $2.21\pm1.11~mm$).
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。
