S3-Tracker: Self-Supervised Surgical Tissue Tracking With Contrastive Random Walks

2026-09-13

AI Summary

本文提出了一种自监督方法S3-Tracker，通过对比随机游走从无标签手术视频中学习点跟踪，解决了在内窥镜视频中因软组织变形而难以进行鲁棒点跟踪的问题。

Abstract

Robust point tracking in endoscopic videos is essential for computer-assisted intervention and autonomous robotic surgery, enabling continuous registration between intraoperative video and preoperative imaging despite soft tissue deformation. However, supervised tracking methods depend on large annotated datasets, while surgical conditions make reliable trajectory annotation challenging. We propose a self-supervised Track-Any-Point approach that learns from unlabeled surgical videos by establishing global pixel correspondences and inferring point trajectories through contrastive random walks. Trained without annotations, our method achieves performance comparable to existing semi-supervised approaches while implicitly handling tissue deformation. These findings demonstrate the feasibility of self-supervised point tracking in surgical environments and its potential to reduce reliance on annotated data.

Problem

• point tracking
• endoscopic videos
• surgical tissue deformation
• self-supervised learning

Innovation

• Self-Supervised
• Contrastive Random Walks
• Surgical Tissue Tracking