用物理仿真生成超声图，实时识别肝脏血管且抗组织变形。
DefSynUS: Real-time Patient-specific Intrahepatic Vessel Identification via Deformation-Aware CT-US Domain Adaptation
- 通过物理渲染合成超声数据，结合形变感知增强提升真实感。
- 在体模和单例临床实验中实现实时血管分支识别，性能稳定。
- 适合无需术前超声的肝手术导航，尤其适用于动态器官场景。
目的：腹腔镜超声（LUS）能实时可视化肝内血管，提升肝切除术安全性。然而受探头限制、血管结构复杂及组织形变影响，血管识别仍具挑战。本文提出一种无需术前超声的实时患者特异性血管识别方法，通过形变感知的超声增强实现鲁棒性。方法：利用术前CT标注的血管信息，通过优化的物理基渲染生成合成超声数据，并与术中超声进行域适应。渲染过程端到端训练，以实现血管识别与患者特异性，无需依赖术前超声。形变感知增强在渲染流程中模拟术中运动与组织形变。结果：在腹部体模和有限临床可行性实验（单病例评估）中，该框架实现了实时肝内血管分支识别，且在新患者姿态下保持性能稳定。结论：该框架可在无术前超声情况下实现实时血管识别，验证了技术可行性，但多患者验证仍需开展以评估泛化性与临床适用性。
原文摘要 · Abstract (English)
Purpose: Laparoscopic ultrasound (LUS) enhances the safety of liver surgery by visualizing intrahepatic vessels in real-time. Still, vessel identification remains difficult due to probe constraints, complex vascular structure, and tissue deformation. This work aims to enable real-time, patient-specific vessel identification that remains robust under deformation through deformable ultrasound augmentation. Methods: Preoperative CT vessel annotations are used to generate synthetic ultrasound data via optimized physics-based rendering, coupled with domain adaptation to intraoperative ultrasound. The rendering is trained end-to-end for vessel identification and patient-specificity, eliminating the need for preoperative ultrasound. A deformation-aware augmentation simulates realistic intraoperative motion and tissue deformation within the rendering pipeline. Results: In abdominal phantom and limited clinical feasibility experiments (single-case clinical evaluation), the framework achieved real-time intrahepatic vessel-branch identification, maintaining performance under new patient poses. Conclusion: The framework enables real-time vessel identification without preoperative ultrasound and supports technical feasibility, but multi-patient validation is still needed for generalizability and clinical feasibility.
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