Endoscopic Spine Surgical View Enhancement Via Diffusion-Prior Contrastive and Physics-Informed Constraints for Robotic Navigation

Abstract

In robot-assisted spinal endoscopy, intraoperative imaging is frequently degraded by bleeding, irrigation fluids, bubbles, smoke, and uneven illumination, which can severely compromise surgical precision, safety, and decisionmaking. Accurate identification of anatomical structures is particularly critical in spinal procedures, yet acquiring paired clean and degraded images in real clinical settings is infeasible. To address this challenge, we propose DCP-Net, an unpaired endoscopic image restoration framework tailored for robotic spinal surgery. DCP-Net integrates Diffusion-Prior Contrastive Learning (DPCL) to leverage generative priors and contrastive objectives for robust latent representations, and Physics-Informed Constraints (PIC) to ensure anatomically consistent restoration. Furthermore, we introduce Diffusion-Prior Uncertainty Estimation (DPUE), providing pixel-wise confidence maps that quantify restoration reliability and guide risk-aware robotic perception. We further constructed a dataset comprising 21,845 paired/unpaired samples of intraoperative visual degradations in spinal endoscopy, primarily involving bleeding, bubbles, and other artifacts. Extensive experiments show that DCP-Net outperforms existing methods in both quantitative metrics and perceptual quality, significantly improving visual clarity and supporting various robotic navigation tasks. Among these tasks, accurate bleeding point detection plays a particularly critical role in ensuring safe and precise navigation in clinical practice.