标题： EndoControlMag：具有周期性参考重置和分层组织感知双掩码控制的鲁棒内窥镜血管运动放大 显示英文标题

标题： EndoControlMag: Robust Endoscopic Vascular Motion Magnification with Periodic Reference Resetting and Hierarchical Tissue-aware Dual-Mask Control

摘要： 在内窥镜手术中可视化细微的血管运动对于手术精度和决策至关重要，但由于手术场景的复杂性和动态性，这仍然具有挑战性。 为了解决这个问题，我们引入了EndoControlMag，这是一种无需训练的基于拉格朗日框架的方法，具有基于掩码的血管运动放大功能，专门针对内窥镜环境设计。 我们的方法包含两个关键模块：一种周期性参考重置（PRR）方案，将视频分成带有动态更新参考帧的短重叠片段，以防止误差累积同时保持时间一致性，以及一种具有双模式掩码膨胀的分层组织感知放大（HTM）框架。 HTM首先使用预训练的视觉跟踪模型跟踪血管核心，以在遮挡和视角变化的情况下保持准确的定位。 然后对周围组织应用两种自适应软化策略中的一种：基于运动的软化，其放大强度与观察到的组织位移成比例；或者基于距离的指数衰减，模拟生物力学力的衰减。 这种双模式方法适应多种手术场景——基于运动的软化在复杂的组织变形中表现出色，而基于距离的软化在不可靠的光流条件下提供稳定性。 我们在涵盖四种不同手术类型的EndoVMM24数据集上评估了EndoControlMag，包括各种具有挑战性的场景，如遮挡、器械干扰、视角变化和血管变形。 定量指标、视觉评估和专家外科医生评价表明，EndoControlMag在放大精度和视觉质量方面显著优于现有方法，同时在具有挑战性的手术条件下保持鲁棒性。 代码、数据集和视频结果可在https://szupc.github.io/EndoControlMag/ 获取。 显示英文摘要

摘要： Visualizing subtle vascular motions in endoscopic surgery is crucial for surgical precision and decision-making, yet remains challenging due to the complex and dynamic nature of surgical scenes. To address this, we introduce EndoControlMag, a training-free, Lagrangian-based framework with mask-conditioned vascular motion magnification tailored to endoscopic environments. Our approach features two key modules: a Periodic Reference Resetting (PRR) scheme that divides videos into short overlapping clips with dynamically updated reference frames to prevent error accumulation while maintaining temporal coherence, and a Hierarchical Tissue-aware Magnification (HTM) framework with dual-mode mask dilation. HTM first tracks vessel cores using a pretrained visual tracking model to maintain accurate localization despite occlusions and view changes. It then applies one of two adaptive softening strategies to surrounding tissues: motion-based softening that modulates magnification strength proportional to observed tissue displacement, or distance-based exponential decay that simulates biomechanical force attenuation. This dual-mode approach accommodates diverse surgical scenarios-motion-based softening excels with complex tissue deformations while distance-based softening provides stability during unreliable optical flow conditions. We evaluate EndoControlMag on our EndoVMM24 dataset spanning four different surgery types and various challenging scenarios, including occlusions, instrument disturbance, view changes, and vessel deformations. Quantitative metrics, visual assessments, and expert surgeon evaluations demonstrate that EndoControlMag significantly outperforms existing methods in both magnification accuracy and visual quality while maintaining robustness across challenging surgical conditions. The code, dataset, and video results are available at https://szupc.github.io/EndoControlMag/.