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Related Experiment Video

Updated: May 11, 2026

Tracking Mouse Bone Marrow Monocytes In Vivo

Tracking Mouse Bone Marrow Monocytes In Vivo

Published on: February 27, 2015

A-MFST: adaptive multi-flow sparse tracker for real-time tissue tracking under occlusion.

Yuxin Chen¹, Zijian Wu², Adam Schmidt³

¹Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, V6T 1Z4, BC, Canada. yuxinchen@ece.ubc.ca.

International Journal of Computer Assisted Radiology and Surgery

|May 22, 2025

Summary

This summary is machine-generated.

This study enhances real-time tissue tracking for robot-assisted surgery by improving occlusion handling. The new model boosts accuracy and reliability during occlusions without sacrificing speed.

Keywords:

Occlusion detection Scene flow Surgical Robotics Tissue tracking

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Last Updated: May 11, 2026

Tracking Mouse Bone Marrow Monocytes In Vivo

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Published on: February 27, 2015

A Protocol for Real-time 3D Single Particle Tracking

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

Area of Science:

Medical Robotics
Computer Vision
Surgical Technology

Background:

Accurate tissue tracking is crucial for robot-assisted surgery.
Existing models like SENDD (Sparse Efficient Neural Depth and Deformation) excel at real-time tracking but struggle with occlusions.
Enhancing occlusion handling is vital for surgical application reliability.

Purpose of the Study:

To extend the SENDD model for improved occlusion detection and tracking consistency.
To maintain real-time performance while addressing limitations in handling occluded surgical scenes.
To enhance the robustness and accuracy of sparse point tracking in dynamic surgical environments.

Main Methods:

Integration of the Segment Anything Model (SAM2) for detecting and masking occlusions caused by surgical instruments.
Development and incorporation of an Adaptive Multi-Flow Sparse Tracker (A-MFST), an unsupervised variant of Multi-Flow Dense Tracker (MFT).
Implementation of forward-backward consistency metrics within A-MFST for enhanced occlusion and uncertainty estimation.

Main Results:

Significant improvements in tracking accuracy under occlusion, with a 12% reduction in Mean Endpoint Error (MEE).
A 6% improvement in averaged accuracy over specified pixel thresholds, demonstrating enhanced precision.
Forward-backward consistency successfully reduced tracking path drift and improved overall robustness without impacting real-time capabilities.

Conclusions:

The enhanced SENDD model, incorporating A-MFST and SAM2, effectively tracks tissue in real-time during instrument and tissue occlusions.
The combined approach improves tracking accuracy and reliability through robust occlusion handling and optimal frame selection.
The A-MFST model reduces tracking errors and maintains real-time performance, making it suitable for demanding surgical applications.