Sstanet: Surgical Spatio-Temporal Aggregation Network for Phase Recognition

Abstract

Surgical phase recognition is a fundamental component of computer-assisted surgery, providing essential contextual understanding of complex surgical workflows. This paper introduces herein SSTANet, an end-to-end network designed for surgical phase recognition and anchored in a novel Surgical Spatio-Temporal Aggregation (SSTA) module. Unlike conventional 3D CNNs or Transformer-based temporal aggregators that rely on heavy convolutional kernels or global self-attention, SSTA adopts a parallel temporal modeling and lightweight fusion strategy to jointly capture local and long-term temporal dependencies while preserving spatial cues. Specifically, SSTA integrates multi-level representations from a ResNet50-based spatial feature extractor and a hybrid Bi-LSTM + TCN temporal feature extractor, followed by an adaptive spatio-temporal projection layer that efficiently balances model performance and computational cost. This design enables fine-grained phase understanding without the complexity of 3D convolutions or highdimensional attention mechanisms. Comprehensive experiments on the Cholec80 dataset demonstrate the effectiveness of the proposed approach, achieving 88.88 % accuracy, 74.07 % Jaccard, 84.43 % precision, and 85.84 % recall, outperforming several state-of-the-art methods. The results confirm that the proposed SSTA module effectively bridges spatial-temporal information and enhances recognition robustness under complex surgical conditions, offering practical potential for real-time computerassisted surgery systems.

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