Abstract
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 this https URL.
Abstract (translated)
在内窥镜手术中，可视化微小的血管运动对于提高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，这一过程仍然极具挑战。为了应对这些难题，我们提出了一种无需训练、基于拉格朗日方法且适用于内窥镜环境的框架——EndoControlMag，该框架专门针对血管运动放大进行了掩码条件优化。 我们的方法包含两个关键模块：周期参考重置（PRR）方案和层次化组织感知放大（HTM）框架。其中： - **周期参考重置（PRR）** 方案将视频分割成一系列短的、具有动态更新参考帧的重叠片段，以防止错误积累并保持时间上的连贯性。 - **层次化组织感知放大（HTM）** 框架包含双模式掩码膨胀。HTM首先使用预训练的视觉跟踪模型来追踪血管核心，即使在存在遮挡和视角变化的情况下也能维持精确的位置定位。随后，它采用两种自适应软化策略之一来处理周围组织：一种是基于运动的软化策略，根据观察到的组织位移调整放大强度；另一种是基于距离的指数衰减策略，模拟生物力学力的减弱。这种双模式方法能够应对各种手术场景——基于运动的软化策略在复杂组织变形的情况下表现优异，而基于距离的软化策略则为不稳定的光学流条件下提供稳定性。 我们利用涵盖四种不同手术类型及多种具有挑战性情景（包括遮挡、器械干扰、视角变化和血管变形）的EndoVMM24数据集对EndoControlMag进行了评估。定量指标、视觉评价以及专家外科医生的评价表明，EndoControlMag在放大精度和视觉质量方面显著优于现有方法，并且能够应对具有挑战性的手术条件。 我们的代码、数据集及视频结果均可在此网址获取：[https://this-url.com]（请将 [https://this-url.com] 替换为实际链接）。
URL
https://arxiv.org/abs/2507.15292
