Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging

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: <inline-formula> <tex-math notation="LaTeX">$2.21\pm1.11~mm$</tex-math> </inline-formula>). <italic>Note to Practitioners</italic>&#x2014;The precise mapping of trajectories has been a bottleneck in developing autonomous intercostal intervention within limited acoustic space. Existing methods, based on external features such as the skin surface or passive markers, fail to capture the acoustic properties of local tissues, leading to significant shadowing when ribs are involved. The proposed method begins by utilizing distinctive anatomical features to extract cartilage bones and stiff ribs through a class-aware segmentation network. To ensure the segmentation accuracy of the shape of the anatomy of interest, a VAE-based boundary-constraint post-processing in manifold space is developed. Subsequently, a dense skeleton graph-based registration is developed to explicitly consider the subcutaneous bone structure, allowing for the precise mapping of intercostal paths from generic templates to individual patients. Results from ten randomly paired CT and US datasets show that the proposed method accurately maps the intercostal path from the template to individual patients, significantly improving accuracy and robustness over previous methods. We believe that the proposed method can further pave the way for autonomous robotic US imaging.