Real-time Visualization Enhancement for Minimally Invasive Surgery Using Particle Filtering and Depth-aware Occlusion Handling
Augmenting Real-time Visualization in Minimally Invasive Surgery with Particle Filtering and Depth-Aware Occlusion Handling
A hybrid framework improves occlusion handling and depth tracking in MIS. It achieves 81.95% occlusion detection accuracy and 0.53 cm depth RMSE. Surgeon feedback highlights clinical relevance for intraoperative navigation.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
confidence score
0.85
View original evidence
cular 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 ag
Open source S3mean absolute error
0.34
View original evidence
cular 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 ag
Open source S3root mean square error
0.53
View original evidence
cular 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 ag
Open source S3mean absolute error
0.65
View original evidence
ainst 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.
Open source S4root mean square error
0.66
View original evidence
ainst 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.
Open source S4frames per second
38.7
View original evidence
ainst 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.
Open source S4
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
cular 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 ag
Open source S3
ainst 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.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org