Causal Spatiotemporal Reasoning Traces for Surgical Video Understanding Under Sparse Procedural Annotations

Abstract

Surgical video understanding is a critical component in the development of context-aware computer-assisted surgery systems. Modern deep learning approaches have demonstrated remarkable capabilities in recognizing surgical phases, instruments, and actions. However, these models inherently rely on massive amounts of densely annotated video data, which is prohibitively expensive and time-consuming to acquire in the medical domain. Furthermore, conventional correlation-based architectures are prone to learning spurious correlations, such as associating the presence of specific background artifacts with certain surgical phases, rather than capturing the genuine causal mechanisms of instrument-tissue interactions. This paper proposes a novel framework centered on causal spatiotemporal reasoning traces designed specifically for surgical video understanding under conditions of sparse procedural annotations. By formulating surgical workflow analysis through the lens of structural causal models, the proposed framework identifies and mitigates confounding factors inherent in surgical scenes. We introduce a mechanism to extract spatiotemporal traces that capture the true causal relationships between surgical instruments and anatomical structures over time. To address the challenge of label sparsity, we implement a weakly supervised alignment technique that maps sparse procedural text to unannotated video segments using causal intervention. Through extensive theoretical analysis and empirical evaluation on standard surgical benchmarks, we demonstrate that incorporating causal reasoning significantly enhances model robustness and generalizability, particularly when only a fraction of the video frames is annotated. The findings indicate a paradigm shift from purely data-driven correlation learning to physically grounded causal reasoning in medical video analysis.