ロボット肋間超音波撮像における自律的US-CT位置合わせのためのクラス認識軟骨セグメンテーション
Class-Aware Cartilage Segmentation for Autonomous US-CT Registration in Robotic Intercostal Ultrasound Imaging
患者ごとの肋骨形状をクラス認識セグメンテーションで抽出し、グラフベースの非剛体位置合わせでCTテンプレートから個別の肋間スキャン経路を高精度にマッピングする手法を提案した。
著者: Zhongliang Jiang, Yunfeng Kang, Yuan Bi, Xuesong Li, Chenyang Li, Nassir Navab
分類: cs.CV, cs.RO
原文アブストラクト
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: $2.21\pm1.11~mm$).
