Long-Duration Occlusion Tissue Tracking Using Contrastive Learning

Overview

This study presents a novel tissue tracking pipeline that integrates dense optical flow estimation with a contrastive learning-based template matching module. The approach effectively addresses long-horizon occlusions by enabling reliable re-identification of tissue regions after extended periods of occlusion or disappearance from the field of view. Validation on public benchmarks demonstrates improved robustness and continuous tracking performance in challenging surgical video sequences.

Background

Accurate tracking of soft-tissue regions during surgery is critical for monitoring tool-tissue interactions and high-risk areas. Traditional tracking methods rely on rigid assumptions, feature descriptors, or model-based approaches, which often fail in the deformable and texture-less surgical environment. Dense optical flow and SLAM-based methods have improved local motion estimation but struggle with long-term occlusions and low-texture surfaces. Recent Tracking Any Point (TAP) techniques offer dense correspondence estimation but lack region-level context and robustness to long-horizon occlusions.

Data Highlights

The proposed method was validated on the SurgT public benchmark and a synthetic dataset designed for long-horizon occlusion scenarios. It demonstrated enhanced tracking continuity and robustness compared to existing methods, particularly in cases where the target tissue was occluded for extended durations or exited and re-entered the field of view.

Key Findings

• Development of a self-supervised template matching method using contrastive loss to learn visual representations for tissue re-identification.
• Integration of dense optical flow estimation with camera localization to track fine-grained tissue motion in real-time.
• Template matching module enables reliable re-identification of tissue regions after long occlusions, mitigating localization drift.
• Pipeline operates without scene mapping, reducing computational complexity while maintaining tracking accuracy.
• Validated on public and synthetic datasets, showing improved robustness in long-horizon occlusion scenarios compared to prior approaches.

Clinical Implications

This approach enhances intraoperative tissue tracking by maintaining continuous localization despite prolonged occlusions, which is critical for surgical navigation and tool interaction monitoring. The real-time capability and robustness to texture-less regions and severe deformations support its potential integration into surgical assistance systems, improving safety and procedural outcomes.

Conclusion

The proposed contrastive learning-augmented tissue tracking pipeline effectively addresses the challenge of long-duration occlusions in surgical videos. By combining dense optical flow with robust template matching, it ensures continuous and accurate tracking of soft tissue regions, advancing the state of the art in surgical scene understanding.

References

1. SurgT Benchmark -- Public Dataset for Surgical Tracking
2. NeuFlow-V2 -- Dense Optical Flow Estimation Method
3. Tracking Any Point (TAP) Methods -- Dense Long-Term Correspondence
4. QATM and Self-TM -- Template Matching Techniques