用粒子滤波与深度感知遮挡处理增强微创手术中的实时可视化
Augmenting Real-time Visualization in Minimally Invasive Surgery with Particle Filtering and Depth-Aware Occlusion Handling
AI总结：
该研究针对微创手术中器械遮挡影响精度的问题，提出整合YOLO、粒子滤波与MiDaS的实时混合框架，实现高准确率的遮挡检测与深度预测，兼具实时性，获外科医生认可具临床应用潜力。
摘要：
我们提出了一种用于微创手术（MIS）中遮挡处理与深度感知跟踪的实时混合框架。手术器械造成的遮挡常遮蔽感兴趣区域（ROI），影响手术精度。为解决该问题，本方法整合了增强型You Only Look Once（YOLO）检测、粒子滤波（PF）以及使用单网络多深度估计精度（MiDaS）的单目深度估计。通过器械与ROI间基于距离的阈值检测遮挡。实验结果显示，器械检测置信度达0.85，遮挡事件处理有效，深度预测平均绝对误差（MAE）为0.34厘米、均方根误差（RMSE）为0.53厘米；基于体模的独立验证与立体深度测量对比，MAE为0.65厘米、RMSE为0.66厘米。该框架还实现了81.95%的遮挡检测准确率，同时保持38.7 FPS的实时性能。初步外科医生反馈表明，所提可视化框架具有临床相关性，有望用于未来MIS中的术中图像引导导航与决策支持。
英文摘要：
We present a real-time, hybrid framework for occlusion handling and depth-aware tracking in minimally invasive surgeries (MIS). Occlusions caused by surgical instruments often obscure the region of interest (ROI), affecting surgical precision. To address this, our approach integrates enhanced You Only Look Once (YOLO) based detection, Particle Filtering (PF), and monocular depth estimation using Multiple Depth Estimation Accuracy with Single Network (MiDaS). Occlusion is detected using a distance-based threshold between instruments and ROI. Experimental results demonstrate robust instrument detection with a confidence score of 0.85, effective occlusion event handling, and accurate depth prediction with an MAE of 0.34 cm and RMSE of 0.53 cm. Independent phantom-based validation against stereo depth measurements yielded an MAE of 0.65 cm and RMSE of 0.66 cm. The framework further achieved an occlusion detection accuracy of 81.95\% while maintaining real-time performance at 38.7 FPS. Preliminary surgeon feedback indicates that the proposed visualization framework is clinically relevant and holds promise for future intraoperative image-guided navigation and decision support in MIS.
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