Phase Aware Diffusion Sampling for Short Surgical Clip Completion with Procedure Phase Temporal Consistency
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Abstract
The rapid advancement of minimally invasive surgery has resulted in an exponential increase in the volume of recorded endoscopic and robotic surgical videos. These videos serve as critical assets for post-operative analysis, surgical training, and the development of autonomous robotic systems. However, surgical video recordings frequently suffer from missing frames, sensor dropouts, transmission latency, or visual occlusions caused by electrocautery smoke and bodily fluids. Completing these short missing clips is essential to maintain a continuous temporal record. Recent approaches utilizing generative artificial intelligence, particularly diffusion models, have demonstrated exceptional capabilities in image and video synthesis. Despite their high perceptual quality, standard diffusion models often fail to preserve the rigid temporal logic required in surgical procedures, frequently hallucinating incorrect instruments or ignoring critical transitions between distinct surgical phases. To address this limitation, we propose a novel generative framework centered on phase-aware diffusion sampling. By explicitly conditioning the reverse diffusion process on surgical phase temporal priors, our model ensures that the synthesized frames adhere to the correct procedural logic. This paper provides a comprehensive analysis of the theoretical underpinnings, methodological implementation, and clinical evaluation of the proposed framework. Our extensive qualitative and quantitative assessments demonstrate that embedding phase-awareness into the sampling trajectory significantly reduces temporal artifacts and improves the structural fidelity of the reconstructed surgical clips.
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