标题： BREA-Depth：支气管镜真实气道几何深度估计 显示英文标题

标题： BREA-Depth: Bronchoscopy Realistic Airway-geometric Depth Estimation

摘要： 单目深度估计在支气管镜检查中可以显著提高实时导航精度，并增强在复杂、分支气道中的干预安全性。 深度基础模型的最新进展在内窥镜场景中显示出前景，但这些模型在支气管镜检查中往往缺乏解剖意识，过度拟合局部纹理而非捕捉全局气道结构，尤其是在模糊的深度线索和照明不良的情况下。 为了解决这个问题，我们提出了Brea-Depth，一种新的框架，将气道特定的几何先验整合到基础模型适应中，用于支气管镜深度估计。 我们的方法引入了一个深度感知的CycleGAN，改进了真实支气管镜图像与从解剖数据中获得的气道几何之间的翻译，有效地弥合了领域差距。 此外，我们引入了一个气道结构意识损失，以在保持平滑过渡和结构完整性的前提下，强制气道腔内的深度一致性。 通过结合解剖先验，Brea-Depth增强了模型的泛化能力，并产生了更鲁棒、准确的3D气道重建。 为了评估解剖现实性，我们引入了Airway Depth Structure Evaluation，一种新的结构一致性度量标准。 我们在收集的离体人类肺部数据集和开放的支气管镜数据集上验证了BREA-Depth，结果表明它在解剖深度保留方面优于现有方法。 显示英文摘要

摘要： Monocular depth estimation in bronchoscopy can significantly improve real-time navigation accuracy and enhance the safety of interventions in complex, branching airways. Recent advances in depth foundation models have shown promise for endoscopic scenarios, yet these models often lack anatomical awareness in bronchoscopy, overfitting to local textures rather than capturing the global airway structure, particularly under ambiguous depth cues and poor lighting. To address this, we propose Brea-Depth, a novel framework that integrates airway-specific geometric priors into foundation model adaptation for bronchoscopic depth estimation. Our method introduces a depth-aware CycleGAN, refining the translation between real bronchoscopic images and airway geometries from anatomical data, effectively bridging the domain gap. In addition, we introduce an airway structure awareness loss to enforce depth consistency within the airway lumen while preserving smooth transitions and structural integrity. By incorporating anatomical priors, Brea-Depth enhances model generalization and yields more robust, accurate 3D airway reconstructions. To assess anatomical realism, we introduce Airway Depth Structure Evaluation, a new metric for structural consistency. We validate BREA-Depth on a collected ex vivo human lung dataset and an open bronchoscopic dataset, where it outperforms existing methods in anatomical depth preservation.