Augmenting Real-time Visualization in Minimally Invasive Surgery with Particle Filtering and Depth-Aware Occlusion Handling

Key points

• Occlusions caused by surgical instruments often obscure the region of interest (ROI), affecting surgical precision.1
• 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).1
• 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.1
• 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.1

Sources (1)

• [1]Augmenting Real-time Visualization in Minimally Invasive Surgery with Particle Filtering and Depth-Aware Occlusion Handling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 02:01 PM

  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.

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