粒子フィルタと深度対応オクルージョン処理による低侵襲手術のリアルタイム可視化拡張
Augmenting Real-time Visualization in Minimally Invasive Surgery with Particle Filtering and Depth-Aware Occlusion Handling
手術器具による遮蔽を検出し、単眼深度推定と粒子フィルタで対象領域を追跡するリアルタイム可視化フレームワークを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Anuska Sarkar, Partha Acharya, Vishnu Nandury, Kaustav Basu, Subhamoy Mandal
分類: eess.IV, cs.RO
原文アブストラクト
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.