几何一致的内窥镜表示用于图像引导导航：基于结构化基础模型适配
Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
AI总结：
提出统一框架，结合合成数据管道与层级感知几何语义适配，学习几何一致且领域鲁棒的图像表示，提升单目内窥镜中的位姿估计与深度预测性能。
摘要：
由于深度线索有限、组织纹理弱、非刚性变形以及跨域外观变化大，单目内窥镜中基于视觉的精确导航十分困难，这些问题使得位姿估计、深度预测和图像-解剖对齐复杂化。尽管最近的视觉基础模型显示出潜力，但它们学到的表示往往几何一致性不足，阻碍了稳定的特征对应，限制了其在后续导航任务中的可靠性。我们提出了一个统一框架，用于学习单目内窥镜中几何一致且领域鲁棒的图像表示。该框架结合了提供精确几何监督的合成数据管道与层级感知几何语义适配，后者是标准LoRA的结构化替代方案，在Transformer层级间选择性插入低秩适配器，并配合逐层训练目标，以鼓励中间特征的几何对应和深层特征的语义一致性。在公开和专有数据集上的实验表明，几何和语义表示质量得到提升，从而在包括位姿估计和单目深度估计在内的下游导航任务上取得更好性能。学到的表示在临床支气管镜中显示出良好的合成到真实迁移能力，并为在有限监督下适配鼻窦镜和结肠镜提供了有用的初始化。该框架还显示出随模型大小和训练数据的良好扩展性。这些结果支持层级感知、几何引导的适配作为内窥镜表示学习的实用方法。
英文摘要：
Accurate vision-based navigation in monocular endoscopy is difficult due to limited depth cues, weak tissue texture, non-rigid deformation, and substantial appearance variation across domains, all of which complicate pose estimation, depth prediction, and image-to-anatomy alignment. Although recent vision foundation models have shown promise, their learned representations often remain insufficiently geometry-consistent, hindering stable feature correspondence and limiting their reliability for downstream navigation tasks. We propose a unified framework for learning geometry-consistent and domain-robust image representations for monocular endoscopy. The framework combines a synthetic data pipeline that provides accurate geometric supervision with Hierarchy-Aware Geometry-Semantic Adaptation, a structured alternative to standard LoRA that inserts low-rank adapters selectively across the transformer hierarchy and couples them with layer-wise training objectives to encourage geometric correspondence in intermediate features and semantic consistency in deeper features. Experiments on public and proprietary datasets show improved geometric and semantic representation quality, leading to better performance on downstream navigation tasks including pose estimation and monocular depth estimation. The learned representations show favorable synthetic-to-real transfer on clinical bronchoscopy and provide a useful initialization for adaptation to sinus endoscopy and colonoscopy under limited supervision. The framework also shows favorable scaling with model size and training data. These results support hierarchy-aware, geometry-guided adaptation as a practical approach for endoscopic representation learning.
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