Purpose: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g., DINOv2), recent works observed its limitations in medical and surgical domain-specific applications. This work presents a low-ranked adaptation (LoRA) of the foundation model for surgical depth estimation. Methods: We design a foundation model-based depth estimation method, referred to as Surgical-DINO, a low-rank adaptation of the DINOv2 for depth estimation in endoscopic surgery. We build LoRA layers and integrate them into DINO to adapt with surgery-specific domain knowledge instead of conventional fine-tuning. During training, we freeze the DINO image encoder, which shows excellent visual representation capacity, and only optimize the LoRA layers and depth decoder to integrate features from the surgical scene. Results: Our model is extensively validated on a MICCAI challenge dataset of SCARED, which is collected from da Vinci Xi endoscope surgery. We empirically show that Surgical-DINO significantly outperforms all the state-of-the-art models in endoscopic depth estimation tasks. The analysis with ablation studies has shown evidence of the remarkable effect of our LoRA layers and adaptation. Conclusion: Surgical-DINO shed some light on the successful adaptation of the foundation models into the surgical domain for depth estimation. There is clear evidence in the results that zero-shot prediction on pre-trained weights in computer vision datasets or naive fine-tuning is not sufficient to use the foundation model in the surgical domain directly. Code is available at https://github.com/BeileiCui/SurgicalDINO.

翻译：目的：机器人手术中的深度估计在三维重建、手术导航及增强现实可视化中至关重要。尽管基础模型（如DINOv2）在包括深度估计在内的诸多视觉任务中表现出色，但近期研究发现其在医学和手术领域特定应用中的局限性。本文提出一种基于低秩适配（LoRA）的基础模型手术深度估计方法。方法：我们设计了一种名为Surgical-DINO的深度估计方法，该方法通过对DINOv2进行低秩适配实现内窥镜手术深度估计。我们构建LoRA层并集成至DINO中，以融入手术领域知识，替代传统微调策略。训练过程中，我们冻结具有优异视觉表征能力的DINO图像编码器，仅优化LoRA层和深度解码器以整合手术场景特征。结果：模型在达芬奇Xi内窥镜手术收集的MICCAI挑战数据集SCARED上进行了充分验证。实验表明，Surgical-DINO在内窥镜深度估计任务中显著优于所有现有最优模型。消融实验分析证实了LoRA层及其适配策略的显著效果。结论：Surgical-DINO为基础模型成功适配手术领域的深度估计任务提供了新思路。结果清晰表明，直接使用计算机视觉数据集的预训练权重进行零样本预测或简单微调，均不足以将基础模型直接应用于手术领域。代码开源地址：https://github.com/BeileiCui/SurgicalDINO。