  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.
