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  1. Home
  2. Tsms-sam2: Multi-scale Temporal Sampling Augmentation And Memory-splitting Pruning For Promptable Video Object Segmentation And Tracking In Surgical Scenarios.
  1. Home
  2. Tsms-sam2: Multi-scale Temporal Sampling Augmentation And Memory-splitting Pruning For Promptable Video Object Segmentation And Tracking In Surgical Scenarios.

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TSMS-SAM2: Multi-scale Temporal Sampling Augmentation and Memory-Splitting Pruning for Promptable Video Object

Guoping Xu1, Hua-Chieh Shao1, You Zhang1

  • 1The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Machine Learning. Health
|January 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
AugmentationDeep LearningMemorySegment Anything 2Video Object Tracking and Segmentation

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This study introduces TSMS-SAM2, a new framework for surgical video object segmentation and tracking. It improves accuracy and efficiency by addressing rapid motion and memory issues in foundation models.

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Surgical Robotics

Background:

  • Foundation models like Segment Anything Model 2 (SAM2) have advanced promptable video object segmentation and tracking (VOST).
  • Applying VOST to surgical videos is difficult due to complex motion and memory redundancy in SAM2, hindering effective learning.
  • Existing methods struggle with the dynamic and intricate nature of surgical environments.

Purpose of the Study:

  • To enhance promptable VOST in surgical videos by developing a novel framework, TSMS-SAM2.
  • To address challenges posed by rapid object motion and memory redundancy in SAM2 for surgical applications.
  • To improve the robustness and efficiency of segmentation in complex surgical scenarios.

Main Methods:

  • Proposed TSMS-SAM2 framework featuring multi-temporal-scale video sampling augmentation for motion variability.
  • Implemented a memory splitting and pruning mechanism to organize and filter past frame features.
  • Utilized EndoVis2017 and EndoVis2018 datasets for evaluation.
  • Main Results:

    • TSMS-SAM2 achieved state-of-the-art performance with mean Dice scores of 95.24±0.96% on EndoVis2017 and 86.73±15.46% on EndoVis2018.
    • Outperformed previous SAM-based and task-specific methods in surgical video segmentation.
    • Ablation studies validated the effectiveness of multiscale temporal augmentation and memory splitting.

    Conclusions:

    • TSMS-SAM2 offers a robust and efficient solution for promptable VOST in complex surgical videos.
    • The proposed framework significantly improves segmentation accuracy by tackling motion dynamics and memory redundancy.
    • The findings highlight the potential of TSMS-SAM2 for advancing computer-assisted surgery and analysis.