Real-time Visualization Enhancement for Minimally Invasive Surgery
Overview
A hybrid framework improves occlusion handling and depth tracking in MIS with 81.95% occlusion detection accuracy and 0.53 cm depth RMSE. Surgeon feedback highlights clinical relevance for intraoperative navigation. (arXiv, 2026-10-14).
Generated from attributed reports · Updated 3 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · confidence score: 0.85 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · mean absolute error: 0.34 other · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · root mean square error: 0.53 other · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · mean absolute error: 0.65 other · Basis not reported · Differing source assertions
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · root mean square error: 0.66 other · Basis not reported · Differing source assertions
“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.”
Exact source · revision 1Source owner not reported
Reported quantity · frames per second: 38.7 other · Basis not reported
“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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsReal-time Visualization Enhancement for Minimally Invasive Surgery Using 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.
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